12 citations · 33 across the 8 of their papers we have counts for
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astro-ph.CO2025★ 7 cited
Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks
Hai Siong Tan
We examine the use of a novel variant of Physics-Informed Neural Networks to predict cosmological parameters from recent supernovae and baryon acoustic oscillations (BAO) datasets.…
cs.LG2025
Evidential Physics-Informed Neural Networks for Scientific Discovery
Hai Siong Tan, Kuancheng Wang, Rafe McBeth
We present the fundamental theory and implementation guidelines underlying Evidential Physics-Informed Neural Network (E-PINN) -- a novel class of uncertainty-aware PINN. It levera…
cs.LG2025
Evidential Physics-Informed Neural Networks
Hai Siong Tan, Kuancheng Wang, Rafe McBeth
We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quanti…