#physics-informed neural networks
34 papers · 1 filter
Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks
Ashutosh Kumar Mishra, Emma Tolley, Nicolas Cerardi
The paper introduces a physics‑informed generative U‑Net that can evolve fuzzy dark matter fields and perform super‑resolution of simulations while enforcing the Schrödinger‑Poisso…
Reliability-calibrated deep residual full-waveform inversion using geometry-invariant physics encoding: synthetic validation and zero-shot Marmousi-2 testing
Deepak Kumar, Jayant Nath Tripathi, Laxmidhar Behera
The paper presents a deep learning pipeline that uses physics‑derived inputs to correct full‑waveform inversion results and provides calibrated, pixel‑wise uncertainty estimates th…
Data-free neural PDE solvers based on Graph Neural Networks and weak forms
Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez +1
The paper introduces a neural network that solves partial differential equations without any training data by using a graph neural network and the weak form of the equations, compu…
PhySR: Physics-Informed Neural Network for Super-Resolution Reconstruction in Radio Synthesis Imaging
Hongkun Yang, Li Zhang, Ming Zhang +1
PhySR is a physics‑informed neural network that directly reconstructs high‑resolution radio images from low‑resolution dirty images by embedding the telescope’s primary beam, PSF c…
Event-Structured Physics-Informed Neural Networks for Differentiable Critical Clearing Boundaries
Baoli Hao, Chenxi Hu, Ming Zhong +1
The paper introduces an event-structured physics‑informed neural network (ES‑PINN) that models pre‑fault, fault‑on, and post‑clearing dynamics to accurately estimate the critical c…
Unbiased Data-Driven Determination of the Nuclear Dipole Amplitude in the Color Glass Condensate
Si-Wei Dai, Haowu Duan, Long-Gang Pang +4
The paper presents a physics‑informed neural‑network method that embeds the Balitsky‑Kovchegov evolution to extract the nuclear gluon dipole amplitude directly from data, yielding…