26 citations · 37 across the 9 of their papers we have counts for
12 papers
Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields
Yixuan Sun, Anirban Samaddar, Sandeep Madireddy
Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-based models (EBMs) are a natu…
Adaptive Sampling for Spatiotemporal Anomaly Monitoring in Wireless Sensor Networks
Guoqing Lu, Yixuan Sun, Yiwen Jiang +1
Long-term environmental monitoring in wireless sensor networks (WSNs) often uses sparse sampling to extend network lifetime, but sparse sensing can miss short-lived, localized, and…
Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory
Yixuan Sun, Srinivas Eswar, Yin Lin +5
Linear systems arise in generating samples and in calculating observables in lattice quantum chromodynamics~(QCD). Solving the Hermitian positive definite systems, which are sparse…
Efficient Flow Matching using Latent Variables
Anirban Samaddar, Yixuan Sun, Viktor Nilsson +1
Flow matching models have shown great potential in image generation tasks among probabilistic generative models. However, most flow matching models in the literature do not explici…
AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing
Yuni Lai, Yulin Zhu, Yixuan Sun +6
Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness via randomized smoothing offers provable guarant…
The Merit of River Network Topology for Neural Flood Forecasting
Nikolas Kirschstein, Yixuan Sun
Climate change exacerbates riverine floods, which occur with higher frequency and intensity than ever. The much-needed forecasting systems typically rely on accurate river discharg…
Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +6
Numerical models of the ocean and ice sheets are crucial for understanding and simulating the impact of greenhouse gases on the global climate. Oceanic processes affect phenomena s…
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Yixuan Sun, Ololade Sowunmi, Romain Egele +3
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage the advanced search algorithms for multiobjectiv…
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
Yixuan Sun, Sami Khairy, Richard B. Vilim +2
Traditional control theory-based methods require tailored engineering for each system and constant fine-tuning. In power plant control, one often needs to obtain a precise represen…
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Yixuan Sun, Elizabeth Cucuzzella, Steven Brus +5
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurrica…
Vapor-liquid equilibrium predictions of n-alkane/nitrogen mixtures using neural networks
Suman Chakraborty, Yixuan Sun, Guang Lin +1
Understanding fluid phase behavior in high pressure and high temperature conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fuele…
Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks
Yixuan Sun, Imad Hanhan, Michael D. Sangid +1
Evaluating the mechanical response of fiber-reinforced composites can be extremely time consuming and expensive. Machine learning (ML) techniques offer a means for faster predictio…