1 citations · 2 across the 4 of their papers we have counts for
6 papers
Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?
Charles Dawson, Van Tran, Max Z. Li +1
Increased deployment of autonomous systems in fields like transportation and robotics have seen a corresponding increase in safety-critical failures. These failures can be difficul…
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV
Allen M. Wang, Alessandro Pau, Cristina Rea +12
The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advance…
RADIUM: Predicting and Repairing End-to-End Robot Failures using Gradient-Accelerated Sampling
Charles Dawson, Anjali Parashar, Chuchu Fan
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of…
Active Disruption Avoidance and Trajectory Design for Tokamak Ramp-downs with Neural Differential Equations and Reinforcement Learning
Allen M. Wang, Oswin So, Charles Dawson +3
The tokamak offers a promising path to fusion energy, but plasma disruptions pose a major economic risk, motivating considerable advances in disruption avoidance. This work develop…
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help
Charles Dawson, Chuchu Fan
To ensure safe, reliable operation of the electrical grid, we must be able to predict and mitigate likely failures. This need motivates the classic security-constrained AC optimal…
A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling
Charles Dawson, Chuchu Fan
Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of…