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20242026
most citedKolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks

197 citations · 229 across the 22 of their papers we have counts for

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7 papers · 1 filter

physics.comp-ph2026

Constitutive-Set Mechanics: variational mechanics on an admissible set of constitutive laws

Altaf Ahmad Lone, R. T. Durai Prabhakaran, Tawqeer Nasir Tak +2

A structural simulation needs a constitutive law, and experiments rarely determine one uniquely: several laws may fit the same data and satisfy the same physical constraints, yet d…

physics.comp-ph2026

Mechanism-resolved phase-field fracture of composite shells with a certified admissible constitutive operator

Altaf Ahmad Lone, R. T. Durai Prabhakaran, Timon Rabczuk +1

Phase-field models of fracture in fibre-reinforced shells are usually formulated against a single closed-form stored energy, so that the stress, consistent tangent and plane-stress…

physics.comp-ph2026

Beyond Residuals: Energy based solutions of partial differential equations using scientific machine learning

Timon Rabczuk, Yizheng Wang

Energy-based approaches provide a natural and physically consistent framework for a large class of partial differential equations arising in solid and fluid mechanics, where the go…

physics.comp-ph2026

Plasolver: Physics-Informed Neural Operators for Elastoplasticity

Yizheng Wang, Mohammad Sadegh Eshaghi, Huadong Zhang +3

Elastoplastic analysis is computationally demanding because its nonlinear, path-dependent constitutive behavior requires incremental loading and repeated iterative solutions. To ad…

physics.comp-ph2025

PENCO: A Physics-Energy-Numerics-Consistent Operator for 3D Phase Field Modeling

Mostafa Bamdad, Mohammad Sadegh Eshaghi, Cosmin Anitescu +2

Accurate and efficient solutions of spatiotemporal partial differential equations (PDEs), such as phase-field models, are fundamental for understanding interfacial dynamics and mic…

physics.comp-ph2025

Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)

Yizheng Wang, Yuzhou Lin, Somdatta Goswami +8

Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especi…

physics.comp-ph2025

Multi-Head Neural Operator for Modelling Interfacial Dynamics

Mohammad Sadegh Eshaghi, Navid Valizadeh, Cosmin Anitescu +3

Interfacial dynamics underlie a wide range of phenomena, including phase transitions, microstructure coarsening, pattern formation, and thin-film growth, and are typically describe…