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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

Numerical approach for MHD Al2O3-water nanofluid transportation inside a permeable medium using innovative computer method by Sheikholeslami, M.

“…Innovative numerical approach was employed to demonstrate nanofluid MHD flow through a porous enclosure. To model porous medium, Darcy law has been employed…”
Get full text

POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition by Fresca, Stefania, Manzoni, Andrea

“…Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models…”
Get full text

Physics-informed multi-LSTM networks for metamodeling of nonlinear structures by Zhang, Ruiyang, Liu, Yang, Sun, Hao

“…This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The basic…”
Get full text

Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization by Zamani, Hoda, Nadimi-Shahraki, Mohammad H., Gandomi, Amir H.

“…This paper presents a novel bio-inspired algorithm inspired by starlings’ behaviors during their stunning murmuration named starling murmuration optimizer…”
Get full text

SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks by Haghighat, Ehsan, Juanes, Ruben

“…In this paper, we introduce SciANN, a Python package for scientific computing and physics-informed deep learning using artificial neural networks. SciANN uses…”
Get full text