Multi-Lambda Isotropic-Kinematic Hardening (ML-IKH)

Quick Reference

• Use when: Multi-timescale thixotropy, stretched-exponential relaxation, distributed structure kinetics

• Parameters: 7N+1 (per_mode) or 6+3N (weighted_sum)

• Key equation: \(\sigma_y = \sigma_{y,0} + k_3 \sum_{i=1}^{N} w_i \lambda_i\) (weighted-sum) or \(\sigma_{total} = \sum_{i=1}^{N} \sigma_i + \eta_{\infty} \dot{\gamma}\) (per-mode)

• Test modes: flow_curve, startup, relaxation, creep, oscillation, laos

• Material examples: Complex thixotropic fluids with multi-scale structural dynamics

class rheojax.models.ikh.ml_ikh.MLIKH(n_modes=2, yield_mode='per_mode')

Bases: IKHBase

Multi-Lambda Isotropic-Kinematic Hardening (ML-IKH) Model.

Extends MIKH to N modes connected in parallel. Each mode evolves its own internal variables (stress, backstress, structural lambda) with distinct timescales (tau_thix_i) and properties.

Two Yield Mode Options:

• per_mode (default): Each mode has independent yield surface. Total stress is sum of mode stresses.

• weighted_sum: Single global yield surface with structure contribution from all modes: σ_y = σ_y0 + k3·Σ(wᵢ·λᵢ)

Per-Mode Parameters (for each mode i=1..N):

G_i: Shear modulus C_i: Backstress modulus gamma_dyn_i: Dynamic recovery sigma_y0_i: Minimal yield stress delta_sigma_y_i: Structural yield stress tau_thix_i: Rebuilding timescale Gamma_i: Breakdown coefficient

Weighted-Sum Parameters:

G: Global shear modulus C: Global hardening modulus gamma_dyn: Global dynamic recovery sigma_y0: Base yield stress k3: Structure-yield coupling tau_thix_i: Per-mode rebuilding timescales Gamma_i: Per-mode breakdown coefficients w_i: Per-mode structure weights

Global Parameters:

eta_inf: High-shear viscosity (both modes)

Supported Protocols:

• FLOW_CURVE: Steady-state stress vs shear rate (analytical solution)

• STARTUP: Transient stress growth at constant shear rate (return mapping)

• RELAXATION: Stress decay at constant strain (ODE formulation via Diffrax)

• CREEP: Strain evolution at constant stress (ODE formulation via Diffrax)

• OSCILLATION: Small amplitude oscillatory shear response

• LAOS: Large amplitude oscillatory shear (return mapping with sinusoidal strain)

Note

Both yield modes (per_mode, weighted_sum) support all protocols. ODE protocols (creep, relaxation) use Diffrax for numerical integration. Return mapping protocols (startup, LAOS) use JAX scan for time stepping.

Parameters:

• n_modes (int) – Number of structural modes (default: 2)

• yield_mode (Literal['per_mode', 'weighted_sum']) – Yield formulation (‘per_mode’ or ‘weighted_sum’)

__init__(n_modes=2, yield_mode='per_mode')

Initialize base model.

model_function(X, params, test_mode=None, **kwargs)

NumPyro model function with protocol-aware dispatch.

Accepts protocol-specific kwargs (gamma_dot, sigma_applied, sigma_0). Falls back to values cached during _fit() if not provided.

Parameters:

• X – Input data

• params – Parameter array or dict from NumPyro

• test_mode – Optional protocol override

• **kwargs – Protocol-specific arguments

Returns:

Predicted response

property n_modes: int

Number of structural modes.

property yield_mode: str

Yield formulation mode (‘per_mode’ or ‘weighted_sum’).

predict_flow_curve(gamma_dot)

Predict steady-state flow curve.

Parameters:

gamma_dot (TypeAliasType) – Array of shear rates

Return type:

TypeAliasType

Returns:

Array of steady-state stresses

predict_startup(t, gamma_dot=1.0, strain=None)

Predict startup shear response.

Parameters:

• t (TypeAliasType) – Time array

• gamma_dot (float) – Applied shear rate (default: 1.0)

• strain (TypeAliasType | None) – Optional strain array (if None, uses gamma_dot * t)

Return type:

TypeAliasType

Returns:

Array of stresses

predict_relaxation(t, sigma_0=100.0)

Predict stress relaxation after step strain.

Parameters:

• t (TypeAliasType) – Time array

• sigma_0 (float) – Initial stress (default: 100.0)

Return type:

TypeAliasType

Returns:

Array of decaying stresses

predict_creep(t, sigma_applied=50.0)

Predict creep response under constant stress.

Parameters:

• t (TypeAliasType) – Time array

• sigma_applied (float) – Applied constant stress (default: 50.0)

Return type:

TypeAliasType

Returns:

Array of strains

predict_laos(t, gamma_0=1.0, omega=1.0)

Predict large amplitude oscillatory shear response.

Parameters:

• t (TypeAliasType) – Time array

• gamma_0 (float) – Strain amplitude (default: 1.0)

• omega (float) – Angular frequency in rad/s (default: 1.0)

Return type:

TypeAliasType

Returns:

Array of stresses

Overview

The ML-IKH (Multi-Lambda IKH) model extends the Maxwell-Isotropic-Kinematic Hardening (MIKH) model to N parallel modes, capturing materials with distributed thixotropic timescales. This is analogous to the relationship between a single Maxwell element and a Generalized Maxwell Model.

Physical motivation:

• Many thixotropic materials exhibit stretched-exponential recovery that cannot be captured by a single timescale

• Structural elements (particles, droplets, polymer chains) may have hierarchical organization with different restructuring rates

• Multi-mode models provide better fit with fewer total parameters than increasing complexity of single-mode equations

Notation Guide

Symbol	Units	Description
\(N\)	—	Number of structural modes
\(\lambda_i\)	—	Structural parameter for mode i (0 = destructured, 1 = structured)
\(\tau_{thix,i}\)	s	Rebuilding timescale for mode i
\(\Gamma_i\)	—	Breakdown coefficient for mode i
\(w_i\)	—	Weight of mode i (weighted_sum formulation only)
\(\sigma_i\)	Pa	Stress contribution from mode i (per_mode formulation only)
\(\alpha_i\)	Pa	Backstress for mode i (per_mode formulation only)
\(\dot{\gamma}^p_i\)	1/s	Plastic strain rate for mode i (per_mode formulation only)

Theoretical Background

The Need for Multiple Structural Modes

Single-timescale thixotropic models often fail to capture the complex structural dynamics observed in real materials. Experimental evidence shows:

1. Stretched-Exponential Recovery:

When a thixotropic material is allowed to recover at rest after pre-shearing, the yield stress often recovers not as a simple exponential:

\[\sigma_y(t) = \sigma_{y,\infty} - (\sigma_{y,\infty} - \sigma_{y,0}) e^{-t/\tau}\]

but rather as a stretched exponential (Kohlrausch-Williams-Watts form):

\[\sigma_y(t) = \sigma_{y,\infty} - (\sigma_{y,\infty} - \sigma_{y,0}) e^{-(t/\tau)^\beta}\]

where \(\beta < 1\) indicates a distribution of timescales. The ML-IKH model captures this behavior naturally through its \(N\) structural modes.

Stretched Exponential Decomposition (KWW)

The Kohlrausch-Williams-Watts (KWW) stretched exponential function:

\[\phi(t) = \exp\left[-(t/\tau_c)^\beta\right]\]

where \(\beta \in (0, 1]\) is the stretch exponent, can be mathematically decomposed into a sum of pure exponentials:

\[\exp\left[-(t/\tau_c)^\beta\right] = \int_0^\infty \rho(\tau) \, e^{-t/\tau} \, d\tau\]

where \(\rho(\tau)\) is the continuous relaxation time distribution.

Discrete Mode Approximation:

For practical computation, this integral is discretized:

\[\phi(t) \approx \sum_{r=1}^{N} w_r \exp(-t/\tau_r)\]

The weights \(w_r\) and timescales \(\tau_r\) are chosen to minimize approximation error over the experimental time window.

Mode Selection Rule:

A fundamental result from the theory of stretched exponentials is that the number of modes N required for accurate representation scales as:

\[\boxed{N \sim \left(\frac{1}{\beta}\right)^2}\]

This provides practical guidance for model complexity:

Mode Selection Based on Stretch Exponent

\(\beta\)	Physical Behavior	Interpretation	N Required
1.0	Pure exponential	Single timescale	1 (use MIKH)
0.7	Mild stretching	Narrow distribution	2
0.5	Moderate stretching	Moderate distribution	4
0.3	Strong stretching	Broad distribution	9-11

Determining \(\beta\) from Experimental Data:

The stretch exponent \(\beta\) can be extracted from recovery experiments:

1. Pre-shear material to destructure (\(\lambda \to 0\))

2. Stop shearing and monitor yield stress recovery \(\sigma_y(t)\)

3. Fit: \(\ln[-\ln(\Delta\sigma_y(t)/\Delta\sigma_{y,max})] = \beta \ln(t/\tau_c)\)

4. Slope gives \(\beta\), intercept gives \(\tau_c\)

A plot of the left-hand side vs \(\ln(t)\) should be linear for KWW behavior.

2. Hierarchical Microstructure:

Complex fluids often have structure at multiple length scales:

• Waxy crude oils: Primary wax crystals, crystal clusters, space-spanning networks

• Colloidal gels: Primary particles, aggregates, aggregate networks

• Polymer solutions: Chain segments, entanglements, transient networks

Each structural level may have distinct kinetics, leading to multiple characteristic timescales for buildup and breakdown.

3. Wei, Solomon & Larson (2018) ML-IKH Framework:

Wei et al. generalized the single-mode IKH model to N modes, demonstrating that:

• N = 2-3 modes typically capture experimental data across a wide range of shear rates and time scales

• Mode timescales should span the experimental frequency/time window

• Physical interpretation: different modes represent different structural “populations” with distinct kinetics

Mathematical Justification

The multi-mode formulation is mathematically justified by viewing the structural parameter as arising from a distribution of relaxation times. If structure recovery is governed by a spectrum of timescales P(\(\tau\)), then:

\[\lambda(t) = \int_0^\infty P(\tau) \lambda_\tau(t) \, d\tau\]

where \(\lambda_\tau(t)\) is the contribution from structures with timescale \(\tau\).

For practical computation, this integral is discretized into N modes:

\[\lambda(t) \approx \sum_{i=1}^{N} w_i \lambda_i(t)\]

with weights \(w_i\) and timescales \(\tau_i\) chosen to span the relevant range.

Physical Foundations

Distributed Structure Kinetics

Real thixotropic materials often have multiple structural populations with different kinetic timescales:

• Waxy crude oils: Primary wax crystals (fast), crystal clusters (medium), space-spanning networks (slow)

• Colloidal gels: Primary bonds (fast), aggregates (medium), network structure (slow)

• Emulsions: Droplet-droplet contacts (fast), floc formation (medium), phase separation (slow)

Each population may build up and break down at different rates, leading to stretched-exponential recovery:

\[\lambda(t) \approx 1 - (1 - \lambda_0) \exp\left[-(t/\tau)^\beta\right]\]

where \(\beta < 1\) indicates a distribution of timescales. The ML-IKH model captures this by superposing \(N\) exponential modes:

\[\lambda(t) = \sum_{i=1}^{N} w_i \lambda_i(t)\]

Timescale Distribution: Physical Interpretation

The distribution of recovery timescales in multi-mode models has concrete physical origins in the hierarchical nature of soft material microstructure.

Fast Modes ( \(\tau\) ~ 0.1–1 s):

• Physical mechanism: Local bond reformation, nearest-neighbor particle rearrangement

• Structural scale: Individual particle contacts, primary bonds

• Activation energy: Low (thermal fluctuations sufficient)

• Experimental signature: Rapid initial stress recovery after flow cessation

Intermediate Modes ( \(\tau\) ~ 1–10 s):

• Physical mechanism: Cluster reorganization, aggregate restructuring

• Structural scale: Multi-particle aggregates (10–100 particles)

• Activation energy: Moderate (cooperative rearrangements)

• Experimental signature: “Shoulder” in recovery curves, non-exponential character

Slow Modes ( \(\tau\) ~ 10–1000 s):

• Physical mechanism: Network-scale rearrangement, large-scale healing

• Structural scale: Percolating network, sample-spanning structure

• Activation energy: High (requires coordinated motion of many particles)

• Experimental signature: Long-time logarithmic aging, incomplete recovery

Connection to Aging Dynamics:

The slowest modes often exhibit power-law rather than exponential kinetics:

\[\lambda_{slow}(t) \sim t^\mu \quad \text{for} \quad t \gg \tau_{max}\]

This suggests connections to glassy dynamics and soft glassy rheology (see Soft Glassy Rheology (SGR) Models).

Analogy to Prony Series

The multi-mode structure is directly analogous to Prony series for viscoelasticity:

• Viscoelasticity: \(G(t) = \sum G_i \exp(-t/\tau_i)\) (Generalized Maxwell)

• Thixotropy: \(\lambda(t) = \sum w_i \lambda_i(t)\) where \(\lambda_i\) evolves with \(\tau_{\text{thix},i}\) (ML-IKH)

Both use discrete mode approximations to represent continuous relaxation spectra.

Governing Equations

The ML-IKH model has two formulations (see Mathematical Formulation for complete equations):

Per-Mode Formulation (N independent yield surfaces):

• Each mode i has state (\(\sigma_i, \alpha_i, \lambda_i\))

• Total stress: \(\sigma_{\text{total}} = \sum \sigma_i + \eta_\infty \dot{\gamma}\)

• Use when: Distinct mechanical populations (e.g., bimodal particle size)

Weighted-Sum Formulation (single yield surface):

• Single state (\(\sigma\), \(\alpha\)) with \(N\) structure parameters \(\lambda_i\)

• Yield stress: \(\sigma_y = \sigma_{y,0} + k_3 \sum w_i \lambda_i\)

• Use when: Single mechanical response with distributed recovery kinetics

Connection to Generalized Maxwell Model

The ML-IKH model relates to the MIKH model exactly as the Generalized Maxwell Model (Prony series) relates to the single Maxwell element:

Property	Single Mode	Multi-Mode
Viscoelasticity	Maxwell element	Generalized Maxwell (Prony series)
Thixotropy	MIKH (single \(\lambda\))	ML-IKH (N \(\lambda_i\))
Recovery	Exponential	Stretched exponential / multi-exponential
Parameters	11	7N+1 (per_mode) or 6+3N (weighted_sum)

Physical Interpretation

Per-Mode Yield Interpretation

In the per_mode formulation, each mode i represents a distinct structural population with its own mechanical properties and kinetics:

\[\sigma_{total} = \sum_{i=1}^{N} \sigma_i + \eta_{\infty} \dot{\gamma}\]

This is a parallel connection of N independent IKH elements, similar to a Generalized Maxwell Model:

Mode 1: [Spring G_1] ─ [Dashpot] ─ [Yield σ_y,1(λ_1)] ─ [Hardening α_1]
Mode 2: [Spring G_2] ─ [Dashpot] ─ [Yield σ_y,2(λ_2)] ─ [Hardening α_2]
...
Mode N: [Spring G_N] ─ [Dashpot] ─ [Yield σ_y,N(λ_N)] ─ [Hardening α_N]
─────────────────────────────────────────────────────────────────────
+ [Solvent viscosity η_∞]

Physical scenarios for per_mode:

1. Bimodal particle size distribution: Large and small particles with different aggregation kinetics

2. Multiple gel networks: e.g., polymer gel + colloidal gel in same suspension

3. Hierarchical structure: Primary bonds (fast kinetics) + secondary networks (slow kinetics)

Weighted-Sum Yield Interpretation

In the weighted_sum formulation, all modes share a single mechanical response but contribute to a common yield stress through their structural parameters:

\[\sigma_y = \sigma_{y,0} + k_3 \sum_{i=1}^{N} w_i \lambda_i\]

This represents distributed kinetics affecting a single yield mechanism:

Single mechanical element: [Spring G] ─ [Dashpot] ─ [Yield σ_y(λ_1...λ_N)] ─ [Hardening α]

With structure from multiple populations:
λ_1 (fast): τ_1 ~ 0.1 s     ─┐
λ_2 (medium): τ_2 ~ 1 s     ─┼─> weighted sum → σ_y(t)
λ_3 (slow): τ_3 ~ 10 s      ─┘

Physical scenarios for weighted_sum:

1. Single particle population with distributed kinetics: Same particles, different local environments

2. Stretched-exponential recovery: Apparent stretched exponential arises from superposition of exponentials

3. Memory effects: Different timescales represent different “memory depths” in the material’s thixotropic history

Two Yield Mode Options

The ML-IKH model supports two formulations selected via yield_mode:

Per-Mode Yield (Default)

Each mode has an independent yield surface. Total stress is the sum of mode stresses.

• Use when:

• Material has distinct yielding events at different strains

• Different structural components have different mechanical properties

• Parallel mechanical connection is appropriate

Governing equations:

\[\sigma_{total} = \sum_{i=1}^{N} \sigma_i + \eta_{\infty} \dot{\gamma}\]

Each mode follows independent MIKH equations.

• Parameters: 7 per mode + 1 global = 7N + 1 total

Weighted-Sum Yield

Single global yield surface with structure contribution from all modes.

\[\sigma_y = \sigma_{y,0} + k_3 \sum_{i=1}^{N} w_i \lambda_i\]

• Use when:

• Material has single mechanical response but distributed recovery kinetics

• You want parsimonious model with fewer parameters

• Physical intuition suggests “average” structure controls yielding

• Parameters: 6 global + 3 per mode = 6 + 3N total

Mathematical Formulation

Per-Mode Formulation

State for each mode i: (\(\sigma_i, \alpha_i, \lambda_i\))

Yield condition per mode:

\[f_i = |\sigma_i - \alpha_i| - \sigma_{y,i}(\lambda_i) \leq 0\]

Yield stress per mode:

\[\sigma_{y,i} = \sigma_{y0,i} + \Delta\sigma_{y,i} \cdot \lambda_i\]

Structure evolution per mode:

\[\dot{\lambda}_i = \frac{1-\lambda_i}{\tau_{thix,i}} - \Gamma_i \lambda_i |\dot{\gamma}^p_i|\]

Backstress evolution per mode:

\[\dot{\alpha}_i = C_i \dot{\gamma}^p_i - \gamma_{dyn,i} |\alpha_i|^{m-1} \alpha_i |\dot{\gamma}^p_i|\]

Total stress:

\[\sigma_{total} = \sum_{i=1}^{N} \sigma_i + \eta_{\infty} \dot{\gamma}\]

Note that each mode has its own plastic strain rate \(\dot{\gamma}^p_i\) determined by its own yield condition. The total strain rate \(\dot{\gamma}\) is the same for all modes (parallel connection assumption).

Weighted-Sum Formulation

Single state: (\(\sigma, \alpha\)) with multiple \(\lambda_i\)

Single yield condition:

\[f = |\sigma - \alpha| - \sigma_y \leq 0\]

Weighted yield stress:

\[\sigma_y = \sigma_{y,0} + k_3 \sum_{i=1}^{N} w_i \lambda_i\]

Multiple structure evolution equations:

\[\dot{\lambda}_i = \frac{1-\lambda_i}{\tau_{thix,i}} - \Gamma_i \lambda_i |\dot{\gamma}^p|\]

Each \(\lambda_i\) evolves independently with its own (\(\tau_i, \Gamma_i\)), but all share the same plastic strain rate \(\dot{\gamma}^p\).

Backstress evolution:

\[\dot{\alpha} = C \dot{\gamma}^p - \gamma_{dyn} |\alpha|^{m-1} \alpha |\dot{\gamma}^p|\]

Total stress:

\[\sigma_{total} = \sigma + \eta_{\infty} \dot{\gamma}\]

Steady-State Analysis

Per-mode steady state:

Each mode reaches its own steady state:

\[\lambda_{ss,i} = \frac{1/\tau_{thix,i}}{1/\tau_{thix,i} + \Gamma_i |\dot{\gamma}|}\]

Including the backstress saturation \(\alpha_{sat,i} = C_i / \gamma_{dyn,i}\) per mode (Dimitriou & McKinley 2014, Eq. 28), the total stress is:

\[\sigma_{ss} = \sum_{i=1}^{N} \left( \frac{C_i}{\gamma_{dyn,i}} + \sigma_{y0,i} + \Delta\sigma_{y,i} \lambda_{ss,i} \right) + \eta_{\infty} |\dot{\gamma}|\]

Weighted-sum steady state:

All \(\lambda_i\) reach steady state with the global plastic strain rate:

\[\lambda_{ss,i} = \frac{1/\tau_{thix,i}}{1/\tau_{thix,i} + \Gamma_i |\dot{\gamma}^p|}\]

Including the global backstress saturation \(\alpha_{sat} = C / \gamma_{dyn}\), the total stress becomes:

\[\sigma_{ss} = \frac{C}{\gamma_{dyn}} + \sigma_{y,0} + k_3 \sum_{i=1}^{N} w_i \lambda_{ss,i} + \eta_{\infty} |\dot{\gamma}|\]

Validity and Assumptions

Valid for:

• Multi-timescale thixotropy: Materials with stretched-exponential or multi-exponential recovery

• Hierarchical microstructure: Multiple structural length/time scales

• Complex thixotropic loops: History-dependent behavior not captured by single-mode models

Assumptions:

• Discrete mode approximation: Continuous relaxation spectrum approximated by N modes

• Independent mode kinetics: No coupling between structural populations (each \(\lambda_i\) evolves independently)

• Affine deformation: Homogeneous flow (no spatial gradients)

• Isothermal: No temperature dependence

Not appropriate for:

• Simple thixotropy: Use Maxwell-Isotropic-Kinematic Hardening (MIKH) instead (simpler, fewer parameters)

• Shear banding: Requires spatial extension

• Temperature-dependent kinetics: Would require additional modes or temperature-dependent parameters

What You Can Learn

From fitting Multi-Lambda IKH to experimental data, you can extract insights about timescale distributions, structural mode coupling, and complex thixotropic behavior.

Parameter Interpretation

\(\lambda_i\) (Multiple Structure Parameters):

Set of \(N\) dimensionless internal variables (\(0 \leq \lambda_i \leq 1\)) tracking distinct structural populations or timescales. For graduate students: Each \(\lambda_i\) evolves independently: \(d\lambda_i/dt = (1 - \lambda_i)/\tau_{\text{thix},i} - \Gamma_i \lambda_i |\dot{\gamma}^p|\). In weighted_sum mode, \(\sigma_y = \sigma_{y,0} + k_3 \sum w_i \lambda_i\) represents distributed kinetics affecting single yield mechanism. In per_mode, each \(\lambda_i\) has independent yield surface. Recovers stretched-exponential via superposition: \(\lambda(t) \approx \sum w_i \exp(-t/\tau_i) \sim \exp[-(t/\tau_c)^\beta]\). For practitioners: Fast mode (\(\tau_1 \sim 0.1\text{--}1\) s) = local bond reformation. Slow mode (\(\tau_N \sim 100\text{--}1000\) s) = network reorganization. Measure via multi-step recovery tests at different rest times.

\(\tau_{\text{thix},i}\) (Recovery Timescale Spectrum):

Set of \(N\) characteristic rebuilding times spanning 2–4 decades. For graduate students: Logarithmic spacing: \(\tau_i = \tau_{\min} \cdot (\tau_{\max}/\tau_{\min})^{(i-1)/(N-1)}\). Span \(\tau_{\max}/\tau_{\min}\) quantifies timescale dispersion. Broader span (>100) requires more modes (\(N \geq 3\text{--}5\)). Connects to Kohlrausch-Williams-Watts stretch exponent \(\beta\) via \(N \sim (1/\beta)^2\). For practitioners: Extract from recovery data fitting. If single-mode MIKH \(R^2\) improvement > 10% with ML-IKH, complex thixotropy confirmed. Typical: \(N = 2\text{--}3\) sufficient for most soft materials.

\(w_i\) (Mode Weights, weighted_sum only):

Normalized weights (\(\sum w_i = 1\)) quantifying relative importance of each structural timescale. For graduate students: In weighted_sum: \(\sigma_y = \sigma_{y,0} + k_3 \sum w_i \lambda_i\). Dominant mode: \(\arg\max(w_i \cdot k_3)\). Weights relate to distribution of structural elements (e.g., particle size distribution in bimodal colloids). For practitioners: \(w_1 = 0.6\), \(w_2 = 0.3\), \(w_3 = 0.1\) means fast mode dominates yield stress evolution. Adjust weights if recovery curves show multi-exponential character.

\(\Gamma_i\) (Mode-Specific Breakdown Coefficients):

Efficiency of shear-induced destructuring for each structural population. For graduate students: Controls mode-specific shear-thinning: \(\lambda_{\text{ss},i} = 1/(1 + \Gamma_i \tau_{\text{thix},i} |\dot{\gamma}|)\). Different \(\Gamma_i\) values enable modeling materials where some structure (weak bonds) breaks easily while other structure (strong crosslinks) persists. For practitioners: Fit from flow curve multi-regime behavior. Example: \(\Gamma_1 \gg \Gamma_2\) means fast mode breaks down at low shear, slow mode only at high shear.

Material Classification

Material Classification from Multi-Lambda IKH Parameters

Parameter Range	Material Behavior	Typical Materials	Processing Implications
\(N = 2\), \(\tau_{\max}/\tau_{\min} < 10\)	Simple bimodal thixotropy	Bidisperse colloids, two-network gels	Moderate complexity, 2-exponential recovery
\(N = 3\text{--}5\), \(\tau_{\max}/\tau_{\min} = 10\text{--}100\)	Complex thixotropy	Waxy crude oils, cement pastes, dense emulsions	Stretched-exponential recovery, history-dependent
\(N > 5\), \(\tau_{\max}/\tau_{\min} > 100\)	Extreme timescale dispersion	Aging soft glasses, hierarchical gels	Requires long-time measurements, non-Fickian
Per-mode: \(G_1 \ll G_2\)	Bimodal elasticity	Filled polymers, composite gels	Distinct mechanical populations
Weighted-sum: \(w_{\text{fast}} > 0.7\)	Fast-mode dominated	Quickly recovering suspensions	Rapid restructuring after flow

Timescale Distribution Strategies

Logarithmic Distribution

For general-purpose fitting, distribute timescales logarithmically:

\[\tau_i = \tau_{min} \cdot \left( \frac{\tau_{max}}{\tau_{min}} \right)^{(i-1)/(N-1)}\]

For \(N = 3\) with \(\tau_{\min} = 0.1\) s and \(\tau_{\max} = 100\) s:

• \(\tau_1 = 0.1\) s (fast mode)

• \(\tau_2 = 3.16\) s (intermediate mode)

• \(\tau_3 = 100\) s (slow mode)

Experimentally-Motivated Distribution

Choose timescales based on observed relaxation data:

1. Fit stretched exponential to recovery data

2. Extract characteristic time \(\tau_c\) and stretch exponent \(\beta\)

3. Use N modes spanning \(\tau_c \cdot 10^{\pm 2/\beta}\) approximately

Prony-Series Approach

Use automatic determination as in Generalized Maxwell fitting:

import numpy as np

# Example: fit N modes to recovery data
# Generate synthetic recovery data
t_recovery = np.linspace(0, 100, 200)
lambda_recovery = 1.0 - 0.8 * np.exp(-t_recovery / 10.0) - 0.2 * np.exp(-t_recovery / 50.0)

# For automatic mode selection, use logarithmic distribution
n_modes = 3
tau_min, tau_max = 1.0, 100.0
tau_values = np.logspace(np.log10(tau_min), np.log10(tau_max), n_modes)
weights = np.ones(n_modes) / n_modes  # Equal weights as initial guess

Industrial Applications

The ML-IKH model is designed for materials with multi-timescale thixotropy that single-mode models cannot capture. This section provides guidance for industrial materials exhibiting stretched-exponential recovery or hierarchical microstructure.

Complex Waxy Crude Oils

Waxy crude oils with broad wax crystal size distributions exhibit stretched-exponential recovery that requires multiple structural modes.

When to use ML-IKH over MIKH:

• Recovery experiments show \(\beta < 0.8\) (stretched exponential fit)

• Yield stress recovery spans >2 decades of time

• Different temperature histories produce different recovery profiles

Mode selection for waxy crudes:

Wax Content	Recommended N	Physical Interpretation
Low (< 5%)	2	Primary crystals + weak network
Medium (5–15%)	3	Primary + secondary aggregates + network
High (> 15%)	4–5	Full hierarchical structure

Typical timescale distribution:

from rheojax.models import MLIKH

# High-wax crude with hierarchical structure
model = MLIKH(n_modes=4, yield_mode='weighted_sum')

# Timescales spanning crystal → network scales
timescales = [1.0, 10.0, 100.0, 1000.0]  # seconds
weights = [0.15, 0.25, 0.35, 0.25]       # Network-dominated

for i, (tau, w) in enumerate(zip(timescales, weights), 1):
    model.parameters.set_value(f"tau_thix_{i}", tau)
    model.parameters.set_value(f"w_{i}", w)

Pipeline restart implications:

Multi-mode recovery means restart pressure depends strongly on shutdown duration:

• Short shutdown (\(t < \tau_1\)): Only fast modes recover, moderate restart pressure

• Long shutdown (\(t > \tau_N\)): All modes recover, maximum restart pressure

• Intermediate: Non-linear pressure increase with rest time

Bidisperse Colloidal Systems

Bidisperse (two particle size) colloidal suspensions naturally produce two-timescale thixotropy from different aggregation kinetics.

Per-mode formulation recommended:

Each particle population has distinct mechanical properties:

# Bidisperse colloid: small + large particles
model = MLIKH(n_modes=2, yield_mode='per_mode')

# Small particles: fast kinetics, lower modulus
model.parameters.set_value("G_1", 200.0)
model.parameters.set_value("tau_thix_1", 0.5)
model.parameters.set_value("sigma_y0_1", 5.0)

# Large particles: slow kinetics, higher modulus
model.parameters.set_value("G_2", 800.0)
model.parameters.set_value("tau_thix_2", 50.0)
model.parameters.set_value("sigma_y0_2", 15.0)

Identifying bidisperse behavior:

• Flow curve shows two distinct shear-thinning regimes

• Startup stress shows double overshoot or shoulder

• Recovery curve is clearly bi-exponential (not stretched)

Drilling Fluids with Hierarchical Clay Structure

Water-based drilling fluids contain clay platelets that organize at multiple length scales: face-face contacts (fast), edge-face networks (slow).

Typical parameters:

Mode	\(\tau_{\text{thix}}\) (s)	Physical Structure	Weight
Fast	0.1-1	Face-face contacts	0.3-0.4
Medium	1-10	Edge-face bonds	0.3-0.4
Slow	10-100	House-of-cards network	0.2-0.3

API rheology connection:

The multi-mode structure explains why API rheology readings at different times after mixing give different values:

• 10-second gel: Dominated by fast modes

• 10-minute gel: Includes slow mode contribution

• The ratio (10-min gel)/(10-sec gel) indicates timescale dispersion

Dense Emulsions and Foams

Concentrated emulsions exhibit multi-timescale thixotropy from droplet rearrangements at different length scales.

Weighted-sum formulation recommended:

Single mechanical response (droplet deformation) with distributed recovery:

# Dense emulsion (φ > 0.7)
model = MLIKH(n_modes=3, yield_mode='weighted_sum')

# Single mechanical modulus (droplet elasticity)
model.parameters.set_value("G", 500.0)
model.parameters.set_value("sigma_y0", 30.0)
model.parameters.set_value("k3", 50.0)

# Distributed recovery from droplet rearrangements
model.parameters.set_value("tau_thix_1", 0.1)   # Local contacts
model.parameters.set_value("tau_thix_2", 1.0)   # Cluster rearrangement
model.parameters.set_value("tau_thix_3", 10.0)  # Network healing

Mode Selection for Industrial Materials

Practical guidelines for choosing N:

1. Start with N=2 and check if fit improves significantly with N=3

2. Use the \(\beta\) rule: If stretched exponential fit gives \(\beta\), then \(N \sim (1/\beta)^2\)

3. Match experimental timescales: Ensure \(\tau_{\min} < t_{\text{experiment,min}}\) and \(\tau_{\max} > t_{\text{experiment,max}}\)

4. Check for overfitting: AIC/BIC should decrease with added modes

\(\beta\) (stretch exponent) to N mapping:

\(\beta\)	Behavior	N Required	Example Materials
0.9-1.0	Near-exponential	1 (use MIKH)	Simple gels
0.7-0.9	Mild stretching	2	Most drilling fluids
0.5-0.7	Moderate stretching	3-4	Waxy crudes, emulsions
0.3-0.5	Strong stretching	5-9	Aging glasses, cements

Data quality requirements:

Multi-mode fitting requires high-quality recovery data:

• Time range: At least 2 decades spanning \(\tau_{\min}\) to \(\tau_{\max}\)

• Data density: 10+ points per decade of time

• Noise level: Signal-to-noise ratio >20 for reliable mode separation

• Protocol: Pre-shear to consistent initial state before recovery

Parameters

Per-Mode Parameters (yield_mode=’per_mode’)

Parameter	Description
G_i	Mode i shear modulus [Pa]
C_i	Mode i kinematic hardening modulus [Pa]
gamma_dyn_i	Mode i dynamic recovery parameter [-]
sigma_y0_i	Mode i minimal yield stress [Pa]
delta_sigma_y_i	Mode i structural yield contribution [Pa]
tau_thix_i	Mode i rebuilding timescale [s]
Gamma_i	Mode i breakdown coefficient [-]
eta_inf	Global high-shear viscosity [Pa·s]

Weighted-Sum Parameters (yield_mode=’weighted_sum’)

Parameter	Description
G	Global shear modulus [Pa]
C	Global kinematic hardening modulus [Pa]
gamma_dyn	Global dynamic recovery [-]
m	AF exponent [-]
sigma_y0	Base yield stress [Pa]
k3	Structure-yield coupling [Pa]
tau_thix_i	Mode i rebuilding timescale [s]
Gamma_i	Mode i breakdown coefficient [-]
w_i	Mode i structure weight [-]
eta_inf	Global high-shear viscosity [Pa·s]

Fitting Guidance

Choosing Number of Modes

1. Start with N=2 and check if fit improves significantly with N=3

2. Use AIC/BIC for model selection

3. Typical: N=2-4 is sufficient for most materials

Akaike Information Criterion (AIC):

\[AIC = 2k - 2\ln(\hat{L})\]

where \(k\) is the number of parameters and \(\hat{L}\) is the maximum likelihood. Choose the model with the lowest AIC.

Rule of thumb: Add a mode only if it reduces residual sum of squares by more than 5-10%.

Initializing Timescales

Distribute \(\tau_i\) logarithmically across expected range:

# For N modes spanning 0.1s to 100s:
tau_values = np.logspace(-1, 2, n_modes)
for i, tau in enumerate(tau_values, 1):
    model.parameters.set_value(f"tau_thix_{i}", tau)

Best practice: Make sure the timescale range encompasses:

• The shortest characteristic time in your data (e.g., fastest startup)

• The longest experimental time (e.g., recovery experiments)

Per-Mode vs Weighted-Sum Selection

Criterion	Per-Mode	Weighted-Sum
Distinct yield events	✓ Better	–
Single yield stress	–	✓ Better
Parameter economy	More params (7N+1)	Fewer params (6+3N)
Physical interpretation	Parallel elements	Distributed kinetics

Fitting Protocol

For per_mode:

1. Fit each mode’s parameters separately to data at different timescales

2. Use long-time recovery data for slow modes (large \(\tau_i\))

3. Use fast startup data for fast modes (small \(\tau_i\))

4. Global optimization to fine-tune

For weighted_sum:

1. Fit global parameters (\(G\), \(C\), \(\sigma_{y0}\)) from startup/flow curve

2. Fix mechanical parameters

3. Fit kinetic parameters (\(\tau_i\), \(\Gamma_i\), \(w_i\)) from recovery data

4. Constrain \(\sum w_i = 1\) for physical interpretation

Parameter Estimation Methods

Multi-mode models present unique parameter estimation challenges due to mode-mode correlations and potential overfitting. This section provides advanced methods for reliable ML-IKH parameter estimation.

Mode Number Selection (AIC/BIC)

Selecting the optimal number of modes N requires balancing fit quality against model complexity.

Information Criteria:

\[\begin{split}AIC &= 2k - 2\ln(\hat{L}) \\ BIC &= k\ln(n) - 2\ln(\hat{L})\end{split}\]

where \(k\) is the number of parameters, \(n\) is the number of data points, and \(\hat{L}\) is the maximum likelihood.

Practical workflow:

import numpy as np
from rheojax.models import MLIKH

def compute_aic_bic(model, X, y_data):
    """Compute AIC and BIC for fitted model."""
    y_pred = model.predict(X)
    n = len(y_data)
    k = model.parameters.n_free  # Number of free parameters

    # Residual sum of squares
    rss = np.sum((y_data - y_pred)**2)

    # Log-likelihood (assuming Gaussian errors)
    sigma2 = rss / n
    log_likelihood = -n/2 * (np.log(2*np.pi*sigma2) + 1)

    aic = 2*k - 2*log_likelihood
    bic = k*np.log(n) - 2*log_likelihood

    return aic, bic

# Compare N=2, 3, 4 modes
results = []
for n_modes in [2, 3, 4]:
    model = MLIKH(n_modes=n_modes, yield_mode='weighted_sum')
    model.fit(X, y_data, test_mode='startup')
    aic, bic = compute_aic_bic(model, X, y_data)
    results.append({'n_modes': n_modes, 'AIC': aic, 'BIC': bic})

# Select model with lowest BIC (more conservative than AIC)
best_n = min(results, key=lambda x: x['BIC'])['n_modes']

Decision rules:

• \(\Delta\text{AIC}\) < 2: Models essentially equivalent

• \(\Delta\text{AIC}\) = 2-10: Some evidence for lower-AIC model

• \(\Delta\text{AIC}\) > 10: Strong evidence for lower-AIC model

• BIC preferred when sample size is moderate (n > 40) for parsimony

Timescale Initialization from Recovery Data

Good initial timescale estimates dramatically improve convergence.

Method 1: Logarithmic derivative analysis

The logarithmic derivative of recovery data reveals characteristic timescales:

import numpy as np

def estimate_timescales_from_recovery(t, lambda_data, n_modes):
    """Estimate timescales from recovery curve shape."""
    # Compute logarithmic derivative
    d_log_lambda = np.gradient(np.log(1 - lambda_data + 1e-10), np.log(t + 1e-10))

    # Find peaks/shoulders in derivative (indicate timescales)
    from scipy.signal import find_peaks
    peaks, _ = find_peaks(-d_log_lambda, prominence=0.1)

    if len(peaks) >= n_modes:
        tau_estimates = t[peaks[:n_modes]]
    else:
        # Fall back to logarithmic distribution
        tau_estimates = np.logspace(
            np.log10(t[1]), np.log10(t[-1]), n_modes
        )

    return tau_estimates

Method 2: Stretched exponential fit

Extract \(\beta\) first, then distribute timescales:

from scipy.optimize import curve_fit

def stretched_exp(t, tau_c, beta):
    return 1 - np.exp(-(t/tau_c)**beta)

# Fit stretched exponential to recovery
popt, _ = curve_fit(stretched_exp, t_recovery, lambda_recovery,
                    p0=[10.0, 0.7], bounds=([0.1, 0.1], [1000, 1.0]))
tau_c, beta = popt

# Distribute timescales around τ_c
n_modes = max(2, int(np.ceil((1/beta)**2)))
tau_range = tau_c * 10**(2/beta)  # Span factor
tau_values = np.logspace(
    np.log10(tau_c / np.sqrt(tau_range)),
    np.log10(tau_c * np.sqrt(tau_range)),
    n_modes
)

Per-Mode vs Weighted-Sum Fitting Strategies

The two ML-IKH formulations require different fitting approaches.

Per-mode strategy (independent yield surfaces):

Each mode can be fit semi-independently:

from rheojax.models import MLIKH

model = MLIKH(n_modes=2, yield_mode='per_mode')

# Stage 1: Fit fast mode to short-time data
mask_fast = t_data < 1.0  # Short times
model.parameters.freeze_except(['G_1', 'tau_thix_1', 'sigma_y0_1', 'Gamma_1'])
model.fit(X_data[:, mask_fast], y_data[mask_fast], test_mode='startup')

# Stage 2: Fit slow mode to long-time data
mask_slow = t_data > 10.0  # Long times
model.parameters.unfreeze_all()
model.parameters.freeze_except(['G_2', 'tau_thix_2', 'sigma_y0_2', 'Gamma_2'])
model.fit(X_data[:, mask_slow], y_data[mask_slow], test_mode='startup')

# Stage 3: Global refinement with all parameters
model.parameters.unfreeze_all()
model.fit(X_data, y_data, test_mode='startup')

Weighted-sum strategy (single yield surface):

Fit mechanical parameters first, then kinetic:

model = MLIKH(n_modes=3, yield_mode='weighted_sum')

# Stage 1: Mechanical parameters from flow curve
model.parameters.freeze(['tau_thix_1', 'tau_thix_2', 'tau_thix_3',
                        'Gamma_1', 'Gamma_2', 'Gamma_3',
                        'w_1', 'w_2', 'w_3'])
model.fit(gamma_dot, sigma_ss, test_mode='flow_curve')

# Stage 2: Kinetic parameters from recovery
model.parameters.unfreeze_all()
model.parameters.freeze(['G', 'C', 'gamma_dyn', 'sigma_y0', 'k3'])
model.fit(t_recovery, lambda_recovery, test_mode='relaxation')

# Stage 3: Global refinement
model.parameters.unfreeze_all()
model.fit(X_combined, y_combined)

Regularization for Correlated Mode Weights

Mode weights \(w_i\) are often correlated, especially when timescales overlap.

Weight normalization constraint:

Enforce \(\sum w_i = 1\) during optimization:

import jax.numpy as jnp

def normalize_weights(w_raw):
    """Softmax normalization ensures sum=1, all positive."""
    return jnp.exp(w_raw) / jnp.sum(jnp.exp(w_raw))

# Use log-weights as free parameters
# w_i = softmax(log_w_i)

Timescale separation constraint:

Prevent modes from collapsing to same timescale:

def timescale_separation_penalty(tau_values, min_ratio=3.0):
    """Penalty for timescales that are too close."""
    tau_sorted = jnp.sort(tau_values)
    ratios = tau_sorted[1:] / tau_sorted[:-1]
    penalty = jnp.sum(jnp.maximum(0, min_ratio - ratios)**2)
    return penalty

Bayesian regularization via priors:

Use informative priors to regularize mode parameters:

import numpyro
import numpyro.distributions as dist

def ml_ikh_bayesian_model(X, y_obs, n_modes):
    # Log-timescales with ordering constraint
    log_tau_base = numpyro.sample('log_tau_base', dist.Normal(1.0, 1.0))
    log_tau_increments = numpyro.sample(
        'log_tau_increments',
        dist.HalfNormal(0.5).expand([n_modes - 1])
    )
    log_tau = jnp.cumsum(jnp.concatenate([
        jnp.array([log_tau_base]),
        log_tau_increments
    ]))
    tau_values = jnp.exp(log_tau)

    # Dirichlet prior for weights (encourages diversity)
    weights = numpyro.sample('weights',
                             dist.Dirichlet(jnp.ones(n_modes)))

    # ... rest of model

This parameterization ensures \(\tau_1 < \tau_2 < \cdots < \tau_n\) automatically.

Bayesian Inference for Multi-Mode Models

Bayesian inference provides uncertainty quantification for mode parameters:

from rheojax.models import MLIKH

model = MLIKH(n_modes=3, yield_mode='weighted_sum')

# Point estimate first (critical for MCMC initialization)
model.fit(X, y, test_mode='startup')

# Bayesian inference
result = model.fit_bayesian(
    X, y,
    num_warmup=1500,      # More warmup for multi-modal posteriors
    num_samples=3000,     # More samples for mode weight uncertainty
    num_chains=4,
    seed=42
)

# Check mode-specific convergence
for i in range(1, 4):
    print(f"Mode {i}:")
    print(f"  τ_thix_{i}: {result.posterior_samples[f'tau_thix_{i}'].mean():.2f} "
          f"± {result.posterior_samples[f'tau_thix_{i}'].std():.2f}")
    print(f"  w_{i}: {result.posterior_samples[f'w_{i}'].mean():.3f} "
          f"± {result.posterior_samples[f'w_{i}'].std():.3f}")

Diagnosing mode identifiability:

• High posterior correlation between \(w_i\) and \(w_j\) indicates modes may be redundant

• Wide posterior for \(\tau_i\) indicates data doesn’t constrain this timescale

• Multimodal posterior indicates consider reducing \(N\) or using ordered parameterization

JAX-First Numerical Implementation

The ML-IKH model uses a JAX-accelerated ODE integration strategy for all protocols. This section describes the internal state vector structure and numerical approach.

State Vector Structure

For ML-IKH with N modes, the state vector is:

y = [σ, A, λ_1, λ_2, ..., λ_N]

Dimension: 2 + N
─────────────────
y[0] = σ       : deviatoric stress [Pa]
y[1] = A       : backstress internal variable (α = C·A) [-]
y[2:2+N] = λ_r : structure parameters for modes 1...N [-]

For the per_mode formulation with N independent yield surfaces:

y = [σ_1, σ_2, ..., σ_N, A_1, A_2, ..., A_N, λ_1, λ_2, ..., λ_N]

Dimension: 3N
─────────────────
y[0:N]     = σ_i : stress for each mode [Pa]
y[N:2N]    = A_i : backstress variable for each mode [-]
y[2N:3N]   = λ_i : structure parameter for each mode [-]

ODE System (Rate-Controlled)

The governing equations for the weighted-sum formulation:

def rhs_mlikh(t, y, gdot, params):
    """ML-IKH right-hand side for ODE integration.

    Args:
        t: Current time
        y: State vector [σ, A, λ_1, ..., λ_N]
        gdot: Applied shear rate γ̇(t)
        params: Model parameters (G, η, C, q, m, k3, w, k1, k2)

    Returns:
        dy/dt: Time derivatives of state vector
    """
    sigma = y[0]
    A = y[1]
    lam = y[2:]  # Shape: (N,)

    # Backstress and effective stress
    sigma_back = params.C * A
    sigma_eff = sigma - sigma_back

    # Weighted yield stress from all modes
    sigma_y = params.k3 * jnp.sum(params.w * lam)

    # Plastic flow rate (Perzyna regularization)
    overstress = jnp.maximum(jnp.abs(sigma_eff) - sigma_y, 0.0)
    gdot_p = (overstress / params.mu_p) * jnp.sign(sigma_eff)

    # Stress evolution (Maxwell element)
    dsigma = params.G * (gdot - gdot_p) - (params.G / params.eta) * sigma

    # Backstress evolution (Armstrong-Frederick)
    fA = (params.q * jnp.abs(A))**params.m * jnp.sign(A)
    dA = gdot_p - fA * jnp.abs(gdot_p)

    # Structure evolution (each mode independent)
    dlam = params.k1 * (1.0 - lam) - params.k2 * lam * jnp.abs(gdot_p)

    return jnp.concatenate([jnp.array([dsigma, dA]), dlam])

Integration Strategy

The model uses RK4 integration with jax.lax.scan for efficient compilation:

@jax.jit
def simulate_rate_control(rhs, t, u_t, y0, params):
    """Integrate ML-IKH under rate control using scan.

    Args:
        rhs: Right-hand side function
        t: Time array
        u_t: Shear rate history γ̇(t)
        y0: Initial state [σ_0, A_0, λ_1,0, ..., λ_N,0]
        params: Model parameters

    Returns:
        y_hist: State history, shape (len(t), 2+N)
    """
    dt = t[1] - t[0]

    def step(carry, inputs):
        ti, ui = inputs
        y_next = rk4_step(rhs, ti, carry, dt, ui, params)
        return y_next, y_next

    _, y_hist = jax.lax.scan(step, y0, (t, u_t))
    return y_hist

Key advantages of JAX implementation:

1. JIT compilation: First call compiles, subsequent calls are fast

2. Automatic differentiation: Enables gradient-based fitting and Bayesian inference

3. Vectorization via vmap: Efficient batch processing over multiple shear rates

4. GPU acceleration: Seamless transfer to GPU for large-scale computations

Protocol-Specific Notes

Protocol	Implementation Notes
Flow curve	For each \(\dot{\gamma}\), set u_t = gamma_dot * ones_like(t), integrate to steady state
Startup	Set u_t = gamma_dot_0 * ones_like(t), track full \(\sigma(t)\) for overshoot
Relaxation	Initial sigma_0 = G * gamma_0 from step strain, set u_t = 0
Creep	Use stress-controlled wrapper with feedback: \(\dot{\gamma}_{n+1} = \dot{\gamma}_n + \kappa(\sigma_0 - \sigma_n)\)
LAOS	Set u_t = gamma_0 * omega * cos(omega * t), extract harmonics from steady-state cycles

Usage

The ML-IKH model is available via:

from rheojax.models import MLIKH

Common workflows:

1. Recovery data fitting: Determine N modes and timescales from rest-time recovery

2. Flow curve + startup: Fit mechanical and kinetic parameters jointly

3. Model selection: Compare N=2 vs N=3 via AIC/BIC

4. Bayesian inference: Quantify uncertainty in mode weights and timescales

Integration with Pipeline:

from rheojax.pipeline import BayesianPipeline

# Multi-mode thixotropic analysis
(BayesianPipeline()
 .load('recovery_data.csv', x_col='time', y_col='yield_stress')
 .fit_nlsq('ml_ikh', n_modes=3, yield_mode='weighted_sum')
 .fit_bayesian(num_samples=2000)
 .plot_pair()  # Check mode correlations
 .save('ml_ikh_3mode_results.hdf5'))

Usage Examples

Per-Mode Initialization

from rheojax.models import MLIKH

# 2-mode model with per-mode yield surfaces
model = MLIKH(n_modes=2, yield_mode='per_mode')

# Fast mode (short τ)
model.parameters.set_value("G_1", 500.0)
model.parameters.set_value("tau_thix_1", 0.5)

# Slow mode (long τ)
model.parameters.set_value("G_2", 500.0)
model.parameters.set_value("tau_thix_2", 10.0)

Weighted-Sum Initialization

# 3-mode model with single global yield surface
model = MLIKH(n_modes=3, yield_mode='weighted_sum')

# Global mechanical parameters
model.parameters.set_value("G", 1000.0)
model.parameters.set_value("sigma_y0", 20.0)
model.parameters.set_value("k3", 40.0)

# Distributed timescales
for i, tau in enumerate([0.1, 1.0, 10.0], 1):
    model.parameters.set_value(f"tau_thix_{i}", tau)
    model.parameters.set_value(f"w_{i}", 1/3)  # Equal weights

Prediction

import numpy as np

t = np.linspace(0, 20, 200)
gamma = 1.0 * t  # Startup shear
X = np.stack([t, gamma])

stress = model.predict(X)

Fitting

import numpy as np

# Generate synthetic startup data
t_data = np.linspace(0, 20, 100)
gamma_data = 1.0 * t_data  # Constant shear rate
X_data = np.stack([t_data, gamma_data])
stress_data = 50.0 * (1.0 - np.exp(-t_data / 5.0)) + 10.0 * t_data

# Fit to data
model.fit(X_data, stress_data, max_iter=500)

# Bayesian inference
result = model.fit_bayesian(
    X_data, stress_data,
    num_warmup=500, num_samples=1000,
    test_mode='startup'
)

Recovery Behavior Comparison

To illustrate the advantage of multi-mode models, consider structure recovery at rest:

Single mode (MIKH):

\[\lambda(t) = 1 - (1 - \lambda_0) e^{-t/\tau}\]

Pure exponential recovery.

Two modes (ML-IKH weighted_sum):

\[\lambda_{avg}(t) = w_1 \lambda_1(t) + w_2 \lambda_2(t)\]

where:

\[\lambda_i(t) = 1 - (1 - \lambda_{0,i}) e^{-t/\tau_i}\]

This produces bi-exponential recovery, which can approximate stretched exponentials.

N modes:

The sum of N exponentials can approximate stretched exponential recovery for \(0.5 \leq \beta \leq 1\) with good accuracy using \(N = 3\text{--}5\) modes.

Relation to MIKH

ML-IKH with N=1 modes is equivalent to MIKH for the per_mode formulation. For weighted_sum, the mapping is:

• sigma_y0 (ML-IKH) ↔ sigma_y0 (MIKH)

• k3 (ML-IKH) ↔ delta_sigma_y (MIKH)

• w_1 = 1.0 implicitly

Limitations and Considerations

Computational cost: ML-IKH with N modes requires tracking 3N state variables (\(\sigma_i, \alpha_i, \lambda_i\) for per_mode) versus 3 for MIKH. Computational cost scales roughly linearly with N due to JAX vmap optimization.

Parameter identifiability: With many modes, parameters may become poorly identifiable. Use regularization or constrain weights/timescales based on physical intuition.

Physical interpretation: While multi-mode models fit data well, direct physical interpretation of individual modes requires caution. The modes represent a mathematical decomposition that may not correspond to distinct physical structures.

References

[1]

Wei, Y., Solomon, M. J., and Larson, R. G. “A multimode structural kinetics constitutive equation for the transient rheology of thixotropic elasto-viscoplastic fluids.” Journal of Rheology, 62(1), 321-342 (2018). DOI: 10.1122/1.4996752 PDF

[2]

Dimitriou, C. J. and McKinley, G. H. “A comprehensive constitutive law for waxy crude oil: a thixotropic yield stress fluid.” Soft Matter, 10(35), 6619-6644 (2014). DOI: 10.1039/C4SM00578C PDF

[3]

Geri, M., Venkatesan, R., Sambath, K., and McKinley, G. H. “Thermokinematic memory and the thixotropic elasto-viscoplasticity of waxy crude oils.” Journal of Rheology, 61(3), 427-454 (2017). DOI: 10.1122/1.4978259 PDF

[4]

Fielding, S. M., et al. “Aging and rheology in soft materials.” Journal of Rheology, 53(1), 39-64 (2009). https://doi.org/10.1122/1.3018902

[5]

Mewis, J. and Wagner, N. J. “Thixotropy.” Advances in Colloid and Interface Science, 147-148, 214-227 (2009). https://doi.org/10.1016/j.cis.2008.09.005

[6]

Kohlrausch, R. “Theorie des elektrischen Rückstandes in der Leidener Flasche.” Annalen der Physik, 167(2), 179-214 (1854). https://doi.org/10.1002/andp.18541670203

[7]

Williams, G. and Watts, D. C. “Non-symmetrical dielectric relaxation behaviour arising from a simple empirical decay function.” Transactions of the Faraday Society, 66, 80-85 (1970). https://doi.org/10.1039/tf9706600080

[8]

de Souza Mendes, P. R. and Thompson, R. L. “A critical overview of elasto-viscoplastic thixotropic modeling.” Journal of Non-Newtonian Fluid Mechanics, 187-188, 8-15 (2012). https://doi.org/10.1016/j.jnnfm.2012.08.006

[9]

Dullaert, K. and Mewis, J. “A structural kinetics model for thixotropy.” Journal of Non-Newtonian Fluid Mechanics, 139(1-2), 21-30 (2006). https://doi.org/10.1016/j.jnnfm.2006.06.002

[10]

Larson, R. G. and Wei, Y. “A review of thixotropy and its rheological modeling.” Journal of Rheology, 63(3), 477-501 (2019). https://doi.org/10.1122/1.5055031

See Also

• Maxwell-Isotropic-Kinematic Hardening (MIKH) — Single-mode thixotropic IKH model (simpler baseline)

• /models/dmt/dmt_local — Alternative multi-timescale thixotropy approach

• /models/generalized_maxwell — Multi-mode viscoelasticity (Prony series)

• Section 3: Advanced Topics (Weeks 7-12) — Advanced multi-mode fitting strategies