255 citations · 405 across the 33 of their papers we have counts for
6 papers · 2 filters
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach
Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana +5
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Rec…
Uncertainty Aware Deep Learning for Particle Accelerators
Kishansingh Rajput, Malachi Schram, Karthik Somayaji
Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics. However, their predictions can be arbitrarily inaccura…
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
Karthik Somayaji NS, Yu Wang, Malachi Schram +4
Risk-sensitive reinforcement learning (RL) has garnered significant attention in recent years due to the growing interest in deploying RL agents in real-world scenarios. A critical…
A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia
Diana McSpadden, Steven Goldenberg, Binata Roy +3
Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and ca…
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput +7
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaus…
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
Yasir Alanazi, Malachi Schram, Kishansingh Rajput +7
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulat…