2 citations · 2 across the 5 of their papers we have counts for
7 papers
Two-Scale Localized PCA-Net: Coarse-Global and Local-Residual Representations for Artifact-Reduced PDE Operator Learning
Mrigank Dhingra, Jordan Stout, Omer San
Localized dimensionality reduction improves the scalability of operator learning for high-dimensional partial differential equations (PDEs), but independently decoded local patches…
Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems
Mrigank Dhingra, Ramchandran Muthukumar, Rebecca Willett +1
Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation. Classical data assimilation…
Stabilizing autoregressive forecasts in chaotic systems via multi-rate latent recurrence
Mrigank Dhingra, Omer San
Long-horizon autoregressive forecasting of chaotic dynamical systems remains challenging due to rapid error amplification and distribution shift: small one-step inaccuracies compou…
Superresolving Non-linear PDE Dynamics with Reduced-Order Autodifferentiable Ensemble Kalman Filtering For Turbulence Modeling and Flow Regulation
Mrigank Dhingra, Omer San
Accurately reconstructing and forecasting high-resolution (HR) states from computationally cheap low-resolution (LR) observations is central to estimation-and-control of spatio-tem…
Localized PCA-Net Neural Operators for Scalable Solution Reconstruction of Elliptic PDEs
Mrigank Dhingra, Romit Maulik, Adil Rasheed +1
Neural operator learning has emerged as a powerful approach for solving partial differential equations (PDEs) in a data-driven manner. However, applying principal component analysi…
FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning
Arth Sojitra, Mrigank Dhingra, Omer San
Deep Operator Networks (DeepONets) have recently emerged as powerful data-driven frameworks for learning nonlinear operators, particularly suited for approximating solutions to par…
Accelerated evolution of Burgers turbulence with coarse projective integration and deep learning
Mrigank Dhingra, Omer San, Anne E. Staples
Simulating turbulence to stationarity is a major bottleneck in many engineering problems of practical importance. The problem can be cast as a multiscale problem involving energy r…