2.1k citations · 3.2k across the 21 of their papers we have counts for
26 papers
An extended deep energy method for thermo-mechanical crack propagation
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh +4
Thermo-mechanical fracture couples transient heat conduction on a cracked domain with a crack that grows as the temperature and the displacement evolve. Neural energy solvers have…
JAXIGA - A software framework for GPU-enabled and differentiable isogeometric analysis
Cosmin Anitescu, Yongjie Jessica Zhang
In this paper, we present JAXIGA, an open-source library for isogeometric analysis based on JAX. The library separates the construction of the mesh from the computations which are…
MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting
Tsung Yeh Hsieh, Cosmin Anitescu, Chunghwan Kim +2
Tracking neurite morphology over time helps characterize structural changes during neuronal development and deterioration, but long-term time-lapse imaging is resource-intensive an…
A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture
Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh +4
Phase-field modeling of brittle fracture removes the need to track cracks explicitly by recasting their evolution as the minimization of an energy functional. In return it requires…
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Anirudh Kalyan, Cosmin Anitescu, Xiaoying Zhuang +3
Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. I…
Deep Energy Method with Large Language Model assistance: an open-source Streamlit-based platform for solving variational PDEs
Yizheng Wang, Cosmin Anitescu, Mohammad Sadegh Eshaghi +3
Physics-informed neural networks (PINNs) in energy form, also known as the deep energy method (DEM), offer advantages over strong-form PINNs such as lower-order derivatives and few…
Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators
Yizheng Wang, Zhongkai Hao, Mohammad Sadegh Eshaghi +4
We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classica…
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…
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…
NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers
Mohammad Sadegh Eshaghi, Cosmin Anitescu, Navid Valizadeh +3
Partial differential equations (PDEs) underpin quantitative descriptions across the physical sciences and engineering, yet high-fidelity simulation remains a major computational bo…
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…
Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
Yizheng Wang, Jinshuai Bai, Mohammad Sadegh Eshaghi +4
AI for PDEs has garnered significant attention, particularly Physics-Informed Neural Networks (PINNs). However, PINNs are typically limited to solving specific problems, and any ch…
Variational Physics-informed Neural Operator (VINO) for Solving Partial Differential Equations
Mohammad Sadegh Eshaghi, Cosmin Anitescu, Manish Thombre +3
Solving partial differential equations (PDEs) is a required step in the simulation of natural and engineering systems. The associated computational costs significantly increase whe…
Artificial intelligence for partial differential equations in computational mechanics: A review
Yizheng Wang, Jinshuai Bai, Zhongya Lin +9
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Scien…
Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams
Mohammad Sadegh Eshaghi, Mostafa Bamdad, Cosmin Anitescu +3
This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework.…
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
Yizheng Wang, Jia Sun, Jinshuai Bai +5
AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural networks (PINNs). The recent advent of K…
CAD-compatible structural shape optimization with a movable Bézier tetrahedral mesh
Jorge López, Cosmin Anitescu, Timon Rabczuk
This paper presents the development of a complete CAD-compatible framework for structural shape optimization in 3D. The boundaries of the domain are described using NURBS while the…
A discontinuous Galerkin method based isogeometric analysis framework for flexoelectricity in micro-architected dielectric solids
Saurav Sharma, Cosmin Anitescu, Timon Rabczuk
Flexoelectricity - the generation of electric field in response to a strain gradient - is a universal electromechanical coupling, dominant only at small scales due to its requireme…
Variational energy based XPINNs for phase field analysis in brittle fracture
Ayan Chakraborty, Cosmin Anitescu, Somdatta Goswami +2
Modeling fracture is computationally expensive even in computational simulations of two-dimensional problems. Hence, scaling up the available approaches to be directly applied to l…
A robust monolithic solver for phase-field fracture integrated with fracture energy based arc-length method and under-relaxation
Ritukesh Bharali, Somdatta Goswami, Cosmin Anitescu +1
The phase-field fracture free-energy functional is non-convex with respect to the displacement and the phase field. This results in a poor performance of the conventional monolithi…
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications
Esteban Samaniego, Cosmin Anitescu, Somdatta Goswami +5
Partial Differential Equations (PDE) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step towards a precise knowle…
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
Somdatta Goswami, Cosmin Anitescu, Souvik Chakraborty +1
We present a new physics informed neural network (PINN) algorithm for solving brittle fracture problems. While most of the PINN algorithms available in the literature minimize the…
Isogeometric analysis with local adaptivity based on a posterior error estimation for elastodynamics
Peng Yu, Cosmin Anitescu, Satyendra Tomar +2
This paper presents a novel methodology of local adaptivity for the frequency-domain analysis of the vibrations of Reissner-Mindlin plates. The adaptive discretization is based on…
Volumetric parametrization from a level set boundary representation with PHT Splines
Chiu Ling Chan, Cosmin Anitescu, Timon Rabczuk
A challenge in isogeometric analysis is constructing analysis-suitable volumetric meshes which can accurately represent the geometry of a given physical domain. In this paper, we p…