Results 41 to 50 of about 2,291 (214)

Physics-Informed Neural Network for Unreacted-Core Shrinking Model of Coal Gasification

Chemical Engineering Transactions, 2021
Physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physical laws into the loss functions of the neural network.
Ming Jian Li, Zengrong Su, Chang He, Bingjian Zhang, Qinglin Chen   +4 more
doaj   +1 more source

Dictionary‐based weak‐form training for noise‐robust series hybrid models with multiplicative unknowns

AIChE Journal, EarlyView.
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho, Akshay Kudva, Parth Shah, Dongheon Lee, Joseph Sang‐Il Kwon   +4 more
wiley   +1 more source

R-PINN: Recovery-type a-posteriori estimator enhanced adaptive PINN

Journal of Computational Physics
In recent years, with the advancements in machine learning and neural networks, algorithms using physics-informed neural networks (PINNs) to solve PDEs have gained widespread applications. While these algorithms are well-suited for a wide range of equations, they often exhibit suboptimal performance when applied to equations with large local gradients,
Rongxin Lu, Jiwei Jia, Young Ju Lee, Zheng Lu, Chen-Song Zhang   +4 more
openaire   +2 more sources

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Physics-informed neural networks for solving forward and inverse Vlasov–Poisson equation via fully kinetic simulation

Machine Learning: Science and Technology, 2023
The Vlasov–Poisson equation is one of the most fundamental models in plasma physics. It has been widely used in areas such as confined plasmas in thermonuclear research and space plasmas in planetary magnetospheres.
Baiyi Zhang, Guobiao Cai, Huiyan Weng, Weizong Wang, Lihui Liu, Bijiao He   +5 more
doaj   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

B-PL-PINN: Stabilizing PINN Training with Bayesian Pseudo Labeling

2025 International Joint Conference on Neural Networks (IJCNN)
Training physics-informed neural networks (PINNs) for forward problems often suffers from severe convergence issues, hindering the propagation of information from regions where the desired solution is well-defined. Haitsiukevich and Ilin (2023) proposed an ensemble approach that extends the active training domain of each PINN based on i) ensemble ...
Rohrhofer, Franz Martin, Geiger, Bernhard, Kevin, Innerebner   +2 more
openaire   +3 more sources

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

A dynamic mesh refinement in Physic-Informed Neural Networks for handling Stiff Partial Differential Equations

Alexandria Engineering Journal
Stiff Partial Differential Equations (PDEs) pose considerable challenges due to the presence of steep gradients, which often lead to large residuals when employing coarse training mesh grids, while excessively refined grids substantially increase ...
Nursyiva Irsalinda, Maharani A. Bakar, Fatimah Noor Harun, Ruwaidiah Idris, Sugiyarto Surono   +4 more
doaj   +1 more source