Data-driven Feynman–Kac Discovery with Applications to Prediction and Data Generation

By Qi Feng, Guang Lin, Purav Matlia, Denny Serdarevic

Published 2025-11-05

Everscope rating

1465.5

Relevance to quantitative trading

8 / 10

Implementation complexity

8 / 10

Reproducibility

3 / 5

About this paper

Methodology: Stochastic SINDy under Risk-Neutral Measure with Physics-Informed DNN. Problem types: Time Series Forecasting, Generative Modeling, Regression, Optimization.

arXiv:2511.08606 · Paper rankings

Open the interactive Everscope explorer for full analysis, charts, and paper battles.