Safa Jamali

Northeastern University

United States

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Articles (17)

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Rheology-informed neural networks are discussed as platforms for prediction of complex rheological behavior such as thixo-elasto-visco-plastic mechanics. By informing RhINNs with the appropriate constitutive laws, unprecedented predictability for unseen data is achieved.

Year:

2023

Year:

2023

Year:

2023

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2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Collaborators (4)

Simon A. Rogers

University of Illinois at Urbana-Champaign

UNITED STATES

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Abhinendra Singh

University of Chicago

UNITED STATES

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Fred Burpo

Department Head

United States Military Academy

UNITED STATES

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Dapeng Bi

Associate Professor

Northeastern University

UNITED STATES

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Safa Jamali

Research Interests

Explore related searches

Contact this professor

Articles (17)

Network physics of attractive colloidal gels: Resilience, rigidity, and phase diagram

Data-driven constitutive meta-modeling of nonlinear rheology via multifidelity neural networks

Unbiased construction of constitutive relations for soft materials from experiments via rheology-informed neural networks

Role of interaction range on the microstructure and dynamics of attractive colloidal systems

Small variations in particle-level interactions lead to large structural heterogeneities in colloidal gels

A rheologist's guideline to data-driven recovery of complex fluids' parameters from constitutive models

A fully physiologically-informed time- and rate-dependent hemorheological constitutive model

Topological origins of yielding in short-ranged weakly attractive colloidal gels

A rheologist's guideline to data-driven recovery of complex fluids' parameters from constitutive models

Characterizing blood hysteresis via tensorial thixo-elasto-viscoplastic modeling

Structure and Dynamics of Force Clusters and Networks in Shear Thickening Suspensions

Digital rheometer twins: Learning the hidden rheology of complex fluids through rheology-informed graph neural networks

Drag reduction and the Vogel exponent of a flexible beam in transient shear flows

The Mnemosyne number and the rheology of remembrance

Thixotropy and rheological hysteresis in blood flow

nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling

Life and death of colloidal bonds control the rate-dependent rheology of gels

Collaborators (4)

Simon A. Rogers

Abhinendra Singh

Fred Burpo

Dapeng Bi

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