6 papers
PhysSAE: Mechanistic Interpretability of PINNs with Sparse Autoencoders
Nandita N. Patil, Eshwar R. A., Gajanan V. Honnavar
Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unknown what physical features th…
Weak Form Recovery of Heston Type Stochastic Dynamics
Sai Sathvik Gullipalli, Eshwar R A, Gajanan V. Honnavar
Estimating the coupled drift, diffusion, and leverage structure of a stochastic-volatility model directly from a price path is an unresolved inverse problem: Kramers--Moyal increme…
Symbolic Weak-form Recovery of 2-D Stochastic Generators
Sai Sathvik Gullipalli, Eshwar R A
Recovering two-dimensional Ito generators from trajectory data is difficult because drift increments have low signal-to-noise, bivariate weak designs can be ill-conditioned, and un…
Action-Inspired Generative Models
Eshwar R. A., Debnath Pal
We introduce Action-Inspired Generative Models (AGMs), a dual-network generative framework motivated by the observation that existing bridge-matching methods assign uniform regress…
Physics-Informed Temporal U-Net for High-Fidelity Fluid Interpolation
Eshwar R. A., Nevin Mathew Thomas, Nehal G +1
Reconstructing high-fidelity fluid dynamics from sparse temporal observations is quite challenging, mainly due to the chaotic and non-linear nature of fluid transport. Standard dee…
Weak-Form Recovery of Stochastic Generators
Eshwar R A, Gajanan V. Honnavar
Data-driven discovery of governing equations is well developed for deterministic systems but much less so for stochastic ones. We present a sparse-symbolic algorithm that recovers…