6 papers
Physics-Aware Neural Operators for Direct Inversion in 3D Photoacoustic Tomography
Jiayun Wang, Yousuf Aborahama, Arya Khokhar +10
Learning physics-constrained inverse operators-rather than post-processing physics-based reconstructions-is a broadly applicable strategy for problems with expensive forward models…
EquiReg: Equivariance Regularized Diffusion for Inverse Problems
Bahareh Tolooshams, Aditi Chandrashekar, Rayhan Zirvi +4
Diffusion models represent the state-of-the-art for solving inverse problems such as image restoration tasks. Diffusion-based inverse solvers incorporate a likelihood term to guide…
Toward the Thermodynamic Limit: Neural Operators for Non-equilibrium Dynamics of Mott Insulators
Miles Waugh, Chuwei Wang, Radu Andrei +4
Mott insulators exhibit complex photoexcitation dynamics under intense optical driving, with potential implications for carrier multiplication beyond the Shockley-Queisser limit. P…
From Vision to Decision: Neuromorphic Control for Autonomous Navigation and Tracking
Chuwei Wang, Eduardo Sebastián, Amanda Prorok +1
Robotic navigation has historically struggled to reconcile reactive, sensor-based control with the decisive capabilities of model-based planners. This duality becomes critical when…
From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics
Nihaal Bhojwani, Chuwei Wang, Hai-Yang Wang +3
Modeling how supermassive black holes co-evolve with their host galaxies is notoriously hard because the relevant physics spans nine orders of magnitude in scale-from milliparsecs…
Coarse Graining with Neural Operators for Simulating Chaotic Systems
Chuwei Wang, Julius Berner, Boris Bonev +6
Accurately predicting the long-term behavior of chaotic systems is crucial for various applications such as climate modeling. However, achieving such predictions typically requires…