150 citations · 439 across the 9 of their papers we have counts for
5 papers · 1 filter
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis +1
In this paper, we propose the augmented physics-informed neural network (APINN), which adopts soft and trainable domain decomposition and flexible parameter sharing to further impr…
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
Ameya D. Jagtap, George Em Karniadakis
Inspired by biological neurons, the activation functions play an essential part in the learning process of any artificial neural network commonly used in many real-world problems.…
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis +1
Physics-informed neural networks (PINNs) have become a popular choice for solving high-dimensional partial differential equations (PDEs) due to their excellent approximation power…
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi +1
We propose a new type of neural networks, Kronecker neural networks (KNNs), that form a general framework for neural networks with adaptive activation functions. KNNs employ the Kr…
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks
Ameya D. Jagtap, Kenji Kawaguchi, George Em Karniadakis
We propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep a…