8 papers
Hybrid-time PINNs: Discontinuity-Aware Physics-Informed Neural Networks for Learning Hybrid Dynamical Systems
Shivanshu Tripathi, Maziar Raissi
Hybrid dynamical systems flow continuously but jump at discrete events. Physics-informed neural networks (PINNs) fit data while enforcing known dynamics, but a single network is sm…
VeraGrid-Agent: Tool-Augmented LLMs for Distribution Optimal Power Flow at the Grid Edge
Shivanshu Tripathi, Hamed Mohsenian-Rad, Maziar Raissi
Language models have demonstrated remarkable success in solving a wide range of tasks. However, answering complex scientific questions about the power flow often requires solving t…
Spectrogram-Based Joint Detection, Localization, and Classification of Events in Continuously Recorded IBR Waveforms
Shivanshu Tripathi, Maziar Raissi, Hamed Mohsenian-Rad
Continuously recorded high-resolution waveform measurements provide rich information about fast power system dynamics. However, they require automated methods to identify events. T…
Online Optimization with Unknown Time-Varying Parameters Using Noisy Gradient Measurements
Shivanshu Tripathi, Fabio Pasqualetti, Maziar Raissi
We study online optimization problems in which the cost function depends on latent, time-varying parameters that are unmeasurable and governed by unknown dynamics. Specifically, we…
Test-Driven Agentic Framework for Reliable Robot Controller
Shivanshu Tripathi, Reza Akbarian Bafghi, Maziar Raissi
In this work, we present a test-driven, agentic framework for synthesizing a deployable low-level robot controller for navigation tasks. Given a 2D map with an image of an ultrason…
Data-Efficient Physics-Informed Learning to Model Synchro-Waveform Dynamics of Grid-Integrated Inverter-Based Resources
Shivanshu Tripathi, Hossein Mohsenzadeh Yazdi, Maziar Raissi +1
Inverter-based resources (IBRs) exhibit fast transient dynamics during network disturbances, which often cannot be properly captured by phasor and SCADA measurements. This shortcom…
Online Optimization with Unknown Time-varying Parameters
Shivanshu Tripathi, Abed AlRahman Al Makdah, Fabio Pasqualetti
In this paper, we study optimization problems where the cost function contains time-varying parameters that are unmeasurable and evolve according to linear, yet unknown, dynamics.…
Fusing Multiple Algorithms for Heterogeneous Online Learning
Darshan Gadginmath, Shivanshu Tripathi, Fabio Pasqualetti
This study addresses the challenge of online learning in contexts where agents accumulate disparate data, face resource constraints, and use different local algorithms. This paper…