4 citations · 10 across the 14 of their papers we have counts for
9 papers · 1 filter
Same Target, Different Origins: Aligning Latent Rollout for Consistent PDE Emulation
Xinying Li, Jing Xu, Wenjun Wang +4
Neural PDE solvers commonly generate multi-step predictions through autoregressive rollout, recursively applying a learned evolution operator and feeding each prediction back as in…
Nested Spatio-Temporal Time Series Forecasting
Yinghao Ai, Yukai Zhou, Ruoxi Jiang +8
Spatiotemporal forecasting is critical for real-world applications like traffic management, yet capturing reliable interactions remains challenging under noisy and non-stationary c…
Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
Xigui Li, Hongwei Zhang, Ruoxi Jiang +6
Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. While penalty-based methods offer…
Accelerating PDE Surrogates via RL-Guided Mesh Optimization
Yang Meng, Ruoxi Jiang, Zhuokai Zhao +3
Deep surrogate models for parametric partial differential equations (PDEs) can deliver high-fidelity approximations but remain prohibitively data-hungry: training often requires th…
Hierarchical Implicit Neural Emulators
Ruoxi Jiang, Xiao Zhang, Karan Jakhar +4
Neural PDE solvers offer a powerful tool for modeling complex dynamical systems, but often struggle with error accumulation over long time horizons and maintaining stability and ph…
Embed and Emulate: Contrastive representations for simulation-based inference
Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
Scientific modeling and engineering applications rely heavily on parameter estimation methods to fit physical models and calibrate numerical simulations using real-world measuremen…
Training neural operators to preserve invariant measures of chaotic attractors
Ruoxi Jiang, Peter Y. Lu, Elena Orlova +1
Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neura…
Deep Stochastic Mechanics
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang +2
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schrödinger equation inspired by stochastic mechanics and generative diffusio…
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
Ruoxi Jiang, Rebecca Willett
This paper explores learning emulators for parameter estimation with uncertainty estimation of high-dimensional dynamical systems. We assume access to a computationally complex sim…