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From the 1 of 5 linked papers with an AI index.

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5 papers

physics.data-an2026

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…

stat.ME2026

Symbolic Weak-form Recovery of 2-D Stochastic Generators

Sai Sathvik Gullipalli, Eshwar R A

The paper introduces a weak‑form SINDy estimator that combines spatial kernels, ridge‑stabilized projection, adaptive LASSO/STLSQ selection, diagonal GLS, and a positive‑semidefini…

stat.ME2026

Data-Driven Weak-form Discovery of Stochastic Systems

Eshwar R A, Gajanan V. Honnavar

We present an algorithm for learning the governing equations of a stochastic dynamical system from trajectory data. It recovers interpretable symbolic expressions for both the drif…

cs.LG2026

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.flu-dyn2026

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…