6 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…
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…
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…
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…