26 hasil

Respecting causality for training physics-informed neural networks

Sifan Wang; Shyam Sankaran; Paris G. Perdikaris

Computer Methods in Applied Mechanics and Engineering · 2024 · Inggris · OpenAlex Scopus

CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method

Pao‐Hsiung Chiu; Jian Cheng Wong; Chin Chun Ooi dkk.

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

A new family of Constitutive Artificial Neural Networks towards automated model discovery

Kevin Linka; Ellen Kuhl

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural and man-made materials in response to mechanical loading. Now, computer science offers a universal solution: Neural Networks. Neural Networks…

A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations

Revanth Mattey; Susanta Ghosh

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

Somdatta Goswami; Minglang Yin; Yue Yu dkk.

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

Han Gao; Matthew J. Zahr; Jianxun Wang

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Jeremy Yu; Lu Lu; Xuhui Meng dkk.

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Chenxi Wu; Min Zhu; Qinyang Tan dkk.

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

Xu Chen; Ba Trung Cao; Yong Yuan dkk.

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

Physics informed neural networks for continuum micromechanics

Alexander Henkes; Henning Wessels; Rolf Mahnken

Computer Methods in Applied Mechanics and Engineering · 2022 · Inggris · OpenAlex Scopus

Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture

Sina Amini Niaki; Ehsan Haghighat; Trevor Campbell dkk.

Computer Methods in Applied Mechanics and Engineering · 2021 · Inggris · OpenAlex Scopus

PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs

Pu Ren; Chengping Rao; Yang Liu dkk.

Computer Methods in Applied Mechanics and Engineering · 2021 · Inggris · OpenAlex Scopus

The Arithmetic Optimization Algorithm

Laith Abualigah; Ali H. Diabat; Seyedali Mirjalili dkk.

Computer Methods in Applied Mechanics and Engineering · 2021 · Inggris · OpenAlex Scopus

This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and…

Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications

Weiguo Zhao; Liying Wang; Seyedali Mirjalili

Computer Methods in Applied Mechanics and Engineering · 2021 · Inggris · OpenAlex Scopus

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Sifan Wang; Hanwen Wang; Paris G. Perdikaris

Computer Methods in Applied Mechanics and Engineering · 2021 · Inggris · OpenAlex Scopus

Unsupervised discovery of interpretable hyperelastic constitutive laws

Moritz Flaschel; Siddhant Kumar; Laura De Lorenzis

Computer Methods in Applied Mechanics and Engineering · 2021 · Inggris · OpenAlex Scopus

We propose a new approach for data-driven automated discovery of isotropic hyperelastic constitutive laws. The approach is unsupervised, i.e., it requires no stress data but only displacement and global force data, which are realistically available through mechanical testing and digital image…

A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches

Wei Li; Martin Z. Bazant; Juner Zhu

Computer Methods in Applied Mechanics and Engineering · 2021 · Inggris · OpenAlex Scopus

Deep generative modeling for mechanistic-based learning and design of metamaterial systems

Liwei Wang; Yu‐Chin Chan; Faez Ahmed dkk.

Computer Methods in Applied Mechanics and Engineering · 2020 · Inggris · OpenAlex Scopus

SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks

Ehsan Haghighat; Rubén Juanes

Computer Methods in Applied Mechanics and Engineering · 2020 · Inggris · OpenAlex Scopus

Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

Ameya D. Jagtap; Ehsan Kharazmi; George Em Karniadakis

Computer Methods in Applied Mechanics and Engineering · 2020 · Inggris · OpenAlex Scopus