A survey of data-driven and physics-informed neural operators, organizing the architecture landscape (DeepONet / FNO / GNO / PINO / Transformer-based / diffusion) along three axes: scalability, generalizability, and data sensitivity.
arXiv preprint (copyright held by authors)Neural OperatorScientific MLDeepONet
A practical focused review of neural operators covering DeepONet, FNO and PCANet with hands-on training strategies; benchmarked on Poisson and linear elasticity, and demonstrated as surrogates for Bayesian inversion - an engineering-ready primer.
arXiv preprint (copyright held by authors)Neural OperatorDeepONetFNO
Proposes a physics-guided hybrid architecture combining UNet and FNO, integrating multi-modal Sentinel-1/2 remote sensing with shallow water equation constraints for end-to-end flood prediction.
See LICENSE fileFlood PredictionDeep LearningPhysics-Guided
Reference implementation library for FNO / DeepONet and other neural operators (MIT) - the de-facto standard for learning 'infinite-dimensional maps' as surrogates.
Introduces neural operators with integral kernels parameterized in Fourier space, enabling resolution-invariant PDE solving - a foundational and highly cited work in physics AI.
arXiv preprint (copyright held by authors)FNONeural OperatorFoundational
A systematic survey of operator learning covering the theory, architectures and error analysis of DeepONet, FNO and their variants - the reference entry point to learning infinite-dimensional maps.
arXiv preprint (copyright held by authors)Operator LearningSurveyDeepONet
