Search Results - "Computer methods in applied mechanics and engineering"

The Arithmetic Optimization Algorithm by Abualigah, Laith, Diabat, Ali, Mirjalili, Seyedali, Abd Elaziz, Mohamed, Gandomi, Amir H.

“…This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic…”
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A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks by Wu, Chenxi, Zhu, Min, Tan, Qinyang, Kartha, Yadhu, Lu, Lu

“…Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations…”
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Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications by Zhao, Weiguo, Wang, Liying, Mirjalili, Seyedali

“…A new bio-inspired optimization algorithm called artificial hummingbird algorithm (AHA) is proposed in this work to solve optimization problems. The AHA…”
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Dwarf Mongoose Optimization Algorithm by Agushaka, Jeffrey O., Ezugwu, Absalom E., Abualigah, Laith

“…This paper proposes a new metaheuristic algorithm called dwarf mongoose optimization algorithm (DMO) to solve the classical and CEC 2020 benchmark functions…”
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A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics by Haghighat, Ehsan, Raissi, Maziar, Moure, Adrian, Gomez, Hector, Juanes, Ruben

“…We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to inversion and surrogate modeling in solid…”
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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems by Yu, Jeremy, Lu, Lu, Meng, Xuhui, Karniadakis, George Em

“…Deep learning has been shown to be an effective tool in solving partial differential equations (PDEs) through physics-informed neural networks (PINNs). PINNs…”
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A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data by Lu, Lu, Meng, Xuhui, Cai, Shengze, Mao, Zhiping, Goswami, Somdatta, Zhang, Zhongqiang, Karniadakis, George Em

“…Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for…”
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On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks by Wang, Sifan, Wang, Hanwen, Perdikaris, Paris

“…Physics-informed neural networks (PINNs) are demonstrating remarkable promise in integrating physical models with gappy and noisy observational data, but they…”
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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications by Samaniego, E., Anitescu, C., Goswami, S., Nguyen-Thanh, V.M., Guo, H., Hamdia, K., Zhuang, X., Rabczuk, T.

“…Partial Differential Equations (PDEs) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step…”
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Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems by Jagtap, Ameya D., Kharazmi, Ehsan, Karniadakis, George Em

“…We propose a conservative physics-informed neural network (cPINN) on discrete domains for nonlinear conservation laws. Here, the term discrete domain…”
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hp-VPINNs: Variational physics-informed neural networks with domain decomposition by Kharazmi, Ehsan, Zhang, Zhongqiang, Karniadakis, George E.M.

“…We formulate a general framework for hp-variational physics-informed neural networks (hp-VPINNs) based on the nonlinear approximation of shallow and deep…”
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Physics-informed neural networks for high-speed flows by Mao, Zhiping, Jagtap, Ameya D., Karniadakis, George Em

“…In this work we investigate the possibility of using physics-informed neural networks (PINNs) to approximate the Euler equations that model high-speed…”
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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data by Sun, Luning, Gao, Han, Pan, Shaowu, Wang, Jian-Xun

“…Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into…”
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A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials by Goswami, Somdatta, Yin, Minglang, Yu, Yue, Karniadakis, George Em

“…Failure trajectories, probable failure zones, and damage indices are some of the key quantities of relevance in brittle fracture mechanics. High-fidelity…”
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Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks by Sukumar, N., Srivastava, Ankit

“…In this paper, we introduce a new approach based on distance fields to exactly impose boundary conditions in physics-informed deep neural networks. The…”
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PPINN: Parareal physics-informed neural network for time-dependent PDEs by Meng, Xuhui, Li, Zhen, Zhang, Dongkun, Karniadakis, George Em

“…Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics…”
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Respecting causality for training physics-informed neural networks by Wang, Sifan, Sankaran, Shyam, Perdikaris, Paris

“…While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date PINNs have not been successful in simulating dynamical…”
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CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method by Chiu, Pao-Hsiung, Wong, Jian Cheng, Ooi, Chinchun, Dao, My Ha, Ong, Yew-Soon

“…In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by…”
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Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems by Gao, Han, Zahr, Matthew J., Wang, Jian-Xun

“…Despite the great promise of the physics-informed neural networks (PINNs) in solving forward and inverse problems, several technical challenges are present as…”
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Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks by Kissas, Georgios, Yang, Yibo, Hwuang, Eileen, Witschey, Walter R., Detre, John A., Perdikaris, Paris

“…Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for…”
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