Physic Informed Neural Network
Physics-informed neural networks (PINNs) integrate physical laws, typically expressed as differential equations, into neural network training to solve complex scientific problems. Current research focuses on improving PINN accuracy and efficiency through architectural innovations like Fourier-based networks, Kolmogorov-Arnold networks, and wavelet-based approaches, as well as advanced optimization strategies such as dual cone gradient descent and DiffGrad. These advancements aim to overcome limitations in handling high-frequency solutions, complex geometries, and stiff equations, ultimately enhancing the applicability of PINNs across diverse scientific and engineering domains, including fluid dynamics, seismology, and materials science.
Papers
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
Sunwoong Yang, Hojin Kim, Yoonpyo Hong, Kwanjung Yee, Romit Maulik, Namwoo Kang
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
Abdul Hannan Mustajab, Hao Lyu, Zarghaam Rizvi, Frank Wuttke
Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks
Pouyan Sajadi, Mostafa Rahmani Dehaghani, Yifan Tang, G. Gary Wang
Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)
Marcin Łoś, Tomasz Służalec, Paweł Maczuga, Askold Vilkha, Carlos Uriarte, Maciej Paszyński
The Physics-Informed Neural Network Gravity Model: Generation III
John Martin, Hanspeter Schaub
Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models
Federica Caforio, Francesco Regazzoni, Stefano Pagani, Elias Karabelas, Christoph Augustin, Gundolf Haase, Gernot Plank, Alfio Quarteroni
Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks
Fabio Pavirani, Gargya Gokhale, Bert Claessens, Chris Develder
The Baldwin Effect in Advancing Generalizability of Physics-Informed Neural Networks
Jian Cheng Wong, Chin Chun Ooi, Abhishek Gupta, Pao-Hsiung Chiu, Joshua Shao Zheng Low, My Ha Dao, Yew-Soon Ong