Improvement of Bayesian PINN Training Convergence in Solving Multi-scale PDEs with Noise
Y Hou, X Li, J Wu - arXiv preprint arXiv:2408.09340, 2024 - arxiv.org
Bayesian Physics Informed Neural Networks (BPINN) have received considerable attention
for inferring differential equations' system states and physical parameters according to noisy …
for inferring differential equations' system states and physical parameters according to noisy …
[PDF][PDF] Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants
I Ramireza, J Pinoa, D Pardob, M Sanzc… - arXiv preprint arXiv …, 2024 - researchgate.net
Transformers are crucial for reliable and efficient power system operations, particularly in
supporting the integration of renewable energy. Effective monitoring of transformer health is …
supporting the integration of renewable energy. Effective monitoring of transformer health is …
Neural Fractional Order Differential Equations with Adjoint Based Training
S SM - papers.ssrn.com
Studying dynamical systems helps assess processes that change dynamically concerning
time in the environment. Neural networks are highly complicated mathematical models that …
time in the environment. Neural networks are highly complicated mathematical models that …