4 papers
Beyond Time Induction: Structured Conditional Neural Solvers for Parametric Stochastic-Volatility Pricing
Jose Miguel Lara Rangel, Mixtli Vazquez Mendoza
Physics-informed neural networks provide a flexible framework for learning parametric PDE solution families without labeled data, yet preserving accurate derivatives across heterog…
Neural Operator Processes for Probabilistic Operator Learning under Partial Observations
Jose Miguel Lara-Rangel, Serge Guillas
Neural operators learn mappings between function spaces, but are typically developed with dense input-output training fields and fully observed inputs at inference. Many scientific…
SPDEBench: An Extensive Benchmark for Learning Stochastic PDEs
Yuantu Zhu, Zheyan Li, Dai Shi +8
Stochastic Partial Differential Equations (SPDEs) driven by random noise play a central role in modeling physical processes with rough spatio-temporal dynamics, such as turbulence…
Learning to Forget using Hypernetworks
Jose Miguel Lara Rangel, Stefan Schoepf, Jack Foster +2
Machine unlearning is gaining increasing attention as a way to remove adversarial data poisoning attacks from already trained models and to comply with privacy and AI regulations.…