Cosmin Anitescu

MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting

Tsung Yeh Hsieh, Cosmin Anitescu, Chunghwan Kim, Victoria A. Webster‐Wood et autres

Tracking neurite morphology over time helps characterize structural changes during neuronal development and deterioration, but long-term time-lapse imaging is resource-intensive and difficult to scale. Forecasting future morphology could reduce this burden. Existing neurite digital-twin models such as gated spatiotemporal attention (gSTA) produce …

A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture

Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh, M. Vahab et autres

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 a discretization dense enough to resolve a localization band whose width is set by a …

Towards unified AI-driven fracture mechanics: the extended deep energy method (XDEM)

Yizheng Wang, Yuzhou Lin, Somdatta Goswami, Luyang Zhao et autres

Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especially effective in fracture mechanics due to its energy-based formulation. Despite these advances, existing DEM approaches require dense collocation …

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Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation

Anirudh Kalyan, Cosmin Anitescu, Xiaoying Zhuang, Timon Rabczuk et autres

Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. In this paper, we introduce Dmsh, a first fully automated reinforcement learning pipeline that unifies geometric decomposition and quadrilateral mesh generation within …

Deep energy method with large language model-assisted geometry modeling: an open-source streamlit-based platform for solving variational PDEs

Yue Wang, Cosmin Anitescu, Mohammad Sadegh Eshaghi, Xiaoying Zhuang et autres

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Pretrain finite element method: A pretraining and warm-start framework for PDEs via physics-informed neural operators

Yizheng Wang, Zhongkai Hao, Mohammad Sadegh Eshaghi, Cosmin Anitescu et autres

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NOWS: Neural Operator Warm Starts for accelerating iterative solvers

Mohammad Sadegh Eshaghi, Cosmin Anitescu, Navid Valizadeh, Yizheng Wang et autres

Partial differential equations (PDEs) underpin quantitative descriptions across the physical sciences and engineering, yet high-fidelity simulation remains a major computational bottleneck for many-query, real-time, and design tasks. Data-driven surrogates can be strikingly fast but are often unreliable when applied outside their training …

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Wave energy assessment in the Pacific Northwest under historical and future climate conditions: a case study of the Canadian and U.S. coasts

Mehrdad Baghaei, Timon Rabczuk, Cosmin Anitescu, Mostafa Bamdad et autres

Abstract This study evaluates the wave energy potential along the Vancouver–Washington–Oregon coastline under different climate change scenarios. Wind and bathymetric data obtained from ECMWF and GEBCO databases were used for the 2000–2015 baseline period, and wave simulations were performed using the MIKE …

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Artificial Intelligence For Partial Differential Equations In Computational Mechanics: A Review

Yizheng Wang, Jinshuai Bai, Zhongya Lin, Qimin Wang et autres

Abstract In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science), which has attracted widespread attention. In AI for Science, using artificial intelligence algorithms to solve …

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Multi-head neural operator for modeling interfacial dynamics

Mohammad Sadegh Eshaghi, Navid Valizadeh, Cosmin Anitescu, Yizheng Wang et autres

Interfacial dynamics, governed by stiff and time-dependent nonlinear PDEs, play a central role in phenomena such as phase transitions, microstructure evolution, pattern formation, and thin-film growth. Solving these PDEs efficiently remains challenging due to multiscale behavior and the high computational cost of …

de, cn (code pays fourni par la source)

PENCO: A Physics–Energy–Numerics–Consistent Operator for 3D phase field modeling

Mostafa Bamdad, Mohammad Sadegh Eshaghi, Cosmin Anitescu, Navid Valizadeh et autres

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, Xiaoying Zhuang et autres

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 fewer hyperparameters, yet dedicated and user-friendly software for energy-form PINNs remains scarce. To address this gap, we …