2 citations · 2 across the 4 of their papers we have counts for
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…
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…
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…