4 papers
A Neural Operator-Based Approach to Symbolic Discovery of PDEs
Sergei Garmaev, Olga Fink
Discovering governing equations from data remains challenging when the underlying dynamics involve nonlocal differential operators, field interactions governed by auxiliary equatio…
A Data-Free Symbolic Regression Approach for Solving Equations
Sergei Garmaev, Vinay Sharma, Olga Fink
Many equations arising in science currently cannot be solved by available analytical techniques and are therefore solved numerically, without yielding explicit symbolic expressions…
Spatiotemporal Imputation with Graph-Informed Flow Matching
Zepeng Zhang, Aref Einizade, Jhony H. Giraldo +1
Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning appr…
Complex Equation Learner: Rational Symbolic Regression with Gradient Descent in Complex Domain
Sergei Garmaev, Maurice Gauché, Olga Fink
Symbolic regression aims to discover interpretable equations from data, yet modern gradient-based methods fail for operators that introduce singularities or domain constraints, inc…