2 citations · 2 across the 12 of their papers we have counts for
16 papers
Reflect After Failure: Brain-Inspired Self-Evolving Agents for Embodied Spatial Intelligence
Xin Li, Xiaohuan Gu, Yifan Wang +6
Vision-Language Models (VLMs) have made substantial progress in spatial reasoning, and self-evolving agents further extend these capabilities to embodied environments by learning f…
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…
Spectral-Embedded Operator Learning for Three-Phase Interfacial Flow: A Ternary Cahn-Hilliard-Navier-Stokes Benchmark
Muhammad Abid, Arth Sojitra, Omer San
Operator-learning surrogates have been benchmarked largely on single-field, single-interface problems, leaving unclear whether architectural choices validated in those settings tra…
Quantum SEDONet: Spectrally-Embedded Quantum Deep Operator Networks for Partial Differential Equations
Muhammad Abid, Arth Sojitra, Bipin Tiwari +1
Quantum DeepONet accelerates neural-operator inference by evaluating an orthogonally parameterized network on a quantum computer, reproducing in ideal simulation the accuracy of it…
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…
WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations
Muhammad Abid, Arth Sojitra, Omer San
This work introduces the Wavelet-Laplace Neural Operator (WLNO), a novel neural operator that fuses Haar wavelet multi-scale spatial decomposition with the Laplace-domain pole-resi…
The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows
Bipin Tiwari, Muhammad Abid, Omer San
Inferring unknown initial states in shock-dominated compressible flows from sparse and noisy measurements is a challenging ill-posed inverse problem due to nonlinear wave interacti…
Digital Twins in Coronary Artery Disease: A Mathematical Roadmap
Alessandro Veneziani, Annalisa Quaini, Marco Tezzele +2
The combination of data and models, enhanced by AI methodologies, leads to the paradigm called Digital Twins. This concept is expected to bring unprecedented support to personalize…
Hyperfastrl: Hypernetwork-based reinforcement learning for unified control of parametric chaotic PDEs
Anil Sapkota, Omer San
Spatiotemporal chaos in fluid systems exhibits severe parametric sensitivity, rendering classical adjoint-based optimal control intractable because each operating regime requires r…
SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
Muhammad Abid, Omer San
Reconstructing high-resolution turbulent flow fields from severely under-resolved observations is a fundamental inverse problem in computational fluid dynamics and scientific machi…
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…
Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation
Muhammad Abid, Omer San
Deep Operator Networks (DeepONets) have emerged as a powerful framework for data-driven operator learning, providing flexible surrogates for nonlinear mappings arising in partial d…
Method of Manufactured Learning for Solver-free Training of Neural Operators
Arth Sojitra, Omer San
Training neural operators to approximate mappings between infinite-dimensional function spaces often requires extensive datasets generated by either demanding experimental setups o…
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…