Results 31 to 40 of about 15,175 (257)

Complex Physics-Informed Neural Network

Journal of Computational Physics
We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optimizing the activation parameters, compleX-PINN achieves high accuracy with just a single hidden layer.
Chenhao Si, Ming Yan 0006, Xin Li, Zhihong Xia   +3 more
openaire   +2 more sources

IDRLnet: A Physics-Informed Neural Network Library

CoRR, 2021
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This paper introduces IDRLnet, a Python toolbox for modeling and solving problems through PINN systematically.
Wei Peng 0010, Jun Zhang 0052, Weien Zhou, Xiaoyu Zhao 0002, Wen Yao 0001, Xiaoqian Chen   +5 more
openaire   +2 more sources

DiffGrad for Physics-Informed Neural Networks

CoRR
20 pages, 14 ...
Jamshaid Ul Rahman, Nimra
openaire   +2 more sources

Feasibility and Safety of Somato‐Cognitive Coordination Therapy for Cerebellar Ataxia Following Pediatric Brain Tumor Treatment

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Cerebellar ataxia after pediatric brain tumor treatment can cause persistent gait, balance, and speech impairment, yet no established rehabilitation strategy exists. Somato‐cognitive coordination therapy (SCCT) is a virtual reality–guided intervention designed to promote sensorimotor integration through visually constrained reaching
Masanobu Takeuchi, Takashi Matsuzawa, Jun Yamanishi, Masahiro Yoshitomi, Junji Ikeda, Shin‐Ichi Tsujimoto, Yasuteru Yamaguchi, Norio Shiba, Dhroov Pathare, Ayumi Kato, Masahiko Hara   +10 more
wiley   +1 more source

Physics-Informed Neural Networks for Microprocessor Thermal Management Model

IEEE Access, 2023
The cooling of microprocessors has emerged as a crucial challenge in enhancing performance. Various studies are being conducted to optimize the structural design for microprocessor cooling.
Hwijae Son, Hyunwoo Cho, Hyung Ju Hwang
doaj   +1 more source

Preconditioning for Physics-Informed Neural Networks

CoRR
Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their practical applications.
Songming Liu, Chang Su, Jiachen Yao, Zhongkai Hao, Hang Su 0006, Youjia Wu, Jun Zhu 0001   +6 more
openaire   +2 more sources

Parareal with a Physics-Informed Neural Network as Coarse Propagator

, 2023
AbstractParallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable
Abdul Qadir Ibrahim, Sebastian Götschel, Daniel Ruprecht   +2 more
openaire   +2 more sources

Probing optimisation in physics-informed neural networks

CoRR, 2023
Accepted at the ICLR 2023 Workshop on Physics for Machine ...
Nayara Fonseca, Veronica Guidetti, Will Trojak   +2 more
openaire   +2 more sources

Microbiome−host proteostasis crosstalk—An emerging perspective on mechanisms and interventions toward healthy longevity

FEBS Letters, EarlyView.
Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley   +1 more source

Physics-Informed Neural Networks

Neural networks have been used extensively in many fields with impressive results.Most applications use data as the sole learning source. Recently, researchers have proposedto use additional information about the latent data that gives birth to information-informedmachine learning.
Georgios E. Stavroulakis, Aliki D. Mouratidou, Georgios A. Drosopoulos   +2 more
  +7 more sources