8 papers · 1 filter
Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors
Alexander Scheinker
Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against. We train a single conditional latent diffusion model th…
Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics
Alexander Scheinker
This work presents a new bidirectional autoregressive latent diffusion approach for predicting the evolution of multiple fields (mass density, pressure, velocity, and magnetic fiel…
Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems
Shaifalee Saxena, Alexander Scheinker
In this paper, we study Mahalanobis-guided latent out-of-distribution (OOD) detection for test-time RL controller switching in nonlinear time-varying systems. RL controllers can qu…
Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift
Shaifalee Saxena, Rafael Fierro, Alexander Scheinker
Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test conditions differ from the training distrib…
Nested Extremum Seeking Converges to Stackelberg Equilibrium
Brad Ratto, Alan Williams, Miroslav Krstić +2
The nested Extremum Seeking (nES) algorithm is a model-free optimization method that has been shown to converge to a neighborhood of a Nash equilibrium. In this work, we demonstrat…
PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion
Mahindra Rautela, Alexander Scheinker, Bradley Love +4
PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstrea…
Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
Mahindra Rautela, Alexander Most, Siddharth Mansingh +9
Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-dis…
Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems
Mahindra Rautela, Alexander Scheinker
Virtual beam diagnostics relies on computationally intensive beam dynamics simulations where high-dimensional charged particle beams evolve through the accelerator. We propose Late…