3.4k citations · 10.2k across the 22 of their papers we have counts for
22 papers
Learning Specialized Activation Functions for Physics-informed Neural Networks
Honghui Wang, Lu Lu, Shiji Song +1
Physics-informed neural networks (PINNs) are known to suffer from optimization difficulty. In this work, we reveal the connection between the optimization difficulty of PINNs and a…
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
Zhongkai Hao, Jiachen Yao, Chang Su +8
While significant progress has been made on Physics-Informed Neural Networks (PINNs), a comprehensive comparison of these methods across a wide range of Partial Differential Equati…
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems
Shunyuan Mao, Ruobing Dong, Lu Lu +3
We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We…
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Min Zhu, Handi Zhang, Anran Jiao +2
Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equ…
MIONet: Learning multiple-input operators via tensor product
Pengzhan Jin, Shuai Meng, Lu Lu
As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several…
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks
Mitchell Daneker, Zhen Zhang, George Em Karniadakis +1
The dynamics of systems biological processes are usually modeled by a system of ordinary differential equations (ODEs) with many unknown parameters that need to be inferred from no…