YS research systems

Publications

Complete research record.

32 publications imported from Google Scholar ↗.

2026

  1. Grain-level micromechanical modeling and assessment of fatigue-critical pores using graph neural networksL Loiodice, KS Stopka, Y Sun, G Lin, MD Sangidnpj Computational Materials
  2. Innovating Saltwater Intrusion Monitoring: Deep Learning-Driven Sensor Fusion for Soil Salinity and Moisture MeasurementKJI Egbe, GR Venegas, Y Sun, M Ghayoomi, F HanGeodata and AI, 100108
  3. Disentangled Compositional Diffusion for Controllable Scientific Data GenerationN Madhuj, MH Parikh, A Samaddar, Y Sun, S Madireddy, JX Wang

2025

  1. Chance-constrained flow matching for high-fidelity constraint-aware generationJ Liang, Y Sun, A Samaddar, S Madireddy, F FiorettoarXiv preprint arXiv:2509.25157
  2. Efficient flow matching using latent variablesA Samaddar, Y Sun, V Nilsson, S MadireddyarXiv preprint arXiv:2505.04486
  3. Spatiotemporal forecasting of the edge localized modes in tokamak plasmas using neural networksA Samaddar, Q Gong, S Madireddy, C Hansen, S Joung, DR Smith, Y Sun, ...Machine Learning: Science and Technology 6 (3), 035041
  4. Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge TheoryY Sun, S Eswar, Y Lin, W Detmold, P Shanahan, X Li, Y Liu, ...arXiv preprint arXiv:2509.10378
  5. Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean ModelingY Sun, R Egele, SHK Narayanan, L Van Roekel, C Gonzales, S Brus, ...arXiv preprint arXiv:2508.16489
  6. Generalizable Implicit Neural Representations via Parameterized Latent Dynamics for Baroclinic Ocean ForecastingG Zhao, X Luo, S Lee, Y Ren, S Yoo, L Van Roekel, B Nadiga, ...arXiv preprint arXiv:2503.21588

2024

  1. Predicting mechanical properties from microstructure images in fiber-reinforced polymers using convolutional neural networksY Sun, I Hanhan, MD Sangid, G LinJournal of Composites Science 8 (10), 387
  2. A safe reinforcement learning algorithm for supervisory control of power plantsY Sun, S Khairy, RB Vilim, R Hu, AJ DaveKnowledge-Based Systems 301, 112312
  3. Streamlining ocean dynamics modeling with fourier neural operators: A multiobjective hyperparameter and architecture optimization approachY Sun, O Sowunmi, R Egele, SHK Narayanan, L Van Roekel, ...Mathematics 12 (10), 1483
  4. Parametric sensitivities of a wind-driven baroclinic ocean using neural surrogatesY Sun, E Cucuzzella, S Brus, SHK Narayanan, B Nadiga, L Van Roekel, ...Proceedings of the Platform for Advanced Scientific Computing Conference, 1-10
  5. Advancing Seawater Intrusion Monitoring through Sensor Fusion and Deep LearningKJ Egbe, GR Venegas, Y Sun, M Ghayoomi, F HanGeotechnical Frontiers 2025, 337-345
  6. AI-Based Adjoints to Diagnose the Sensitivity of Ocean ModelsSHK Narayanan, Y Sun, E Cucuzzella, SR Brus, BT Nadiga, ...American Geophysical Union, Ocean Sciences Meeting, DO24A-2506

2023

  1. Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivityZ Guo, P Roy Chowdhury, Z Han, Y Sun, D Feng, G Lin, X Ruannpj Computational Materials 9 (1), 95
  2. Deepgraphonet: A deep graph operator network to learn and zero-shot transfer the dynamic response of networked systemsY Sun, C Moya, G Lin, M YueIEEE Systems Journal 17 (3), 4360-4370
  3. Artificial intelligence guided thermoelectric materials design and discoveryG Han, Y Sun, Y Feng, G Lin, N LuAdvanced Electronic Materials 9 (8), 2300042
  4. Is one epoch all you need for multi-fidelity hyperparameter optimization?R Egele, I Guyon, Y Sun, P BalaprakasharXiv preprint arXiv:2307.15422
  5. Parallel multi-objective hyperparameter optimization with uniform normalization and bounded objectivesR Egele, T Chang, Y Sun, V Vishwanath, P BalaprakasharXiv preprint arXiv:2309.14936
  6. Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean ModelsY Sun, E Cucuzzella, S Brus, SHK Narayanan, B Nadiga, L Van Roekel, ...NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning
  7. Introduction to Reinforcement LearningY Sun, K Raghavan, P BalaprakashMethods and Applications of Autonomous Experimentation, 152-174

2022

  1. A data-centric weak supervised learning for highway traffic incident detectionY Sun, T Mallick, P Balaprakash, J MacfarlaneAccident Analysis & Prevention 176, 106779
  2. Vapor-liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networksS Chakraborty, Y Sun, G Lin, L QiaoJournal of Computational and Applied Mathematics 408, 114059
  3. Artificial intelligence inferred microstructural properties from voltage-capacity curvesY Sun, S Mitra Ayalasomayajula, A Deva, G Lin, RE GarcíaScientific Reports 12 (1), 13421

2021

  1. Machine learning regression guided thermoelectric materials discovery-a reviewG Han, Y Sun, Y Feng, G Lin, N LuES Materials and Manufacturing 14 (18), 20-35

2020

  1. Effective risk prediction of tailings ponds using machine learningJ Yang, Y Sun, Q Li, Y Sun2020 3rd International Conference on Advanced Electronic Materials
  2. A Wavelet-CNN-LSTM Model for Tailings Pond Risk PredictionJ Yang, Q Li, Y SunarXiv preprint arXiv:2010.00518

2019

  1. Infrared thermal imaging-based crack detection using deep learningJ Yang, W Wang, G Lin, Q Li, Y Sun, Y SunIEEE Access 7, 182060-182077
  2. Probabilistic state estimation approach for AC/MTDC distribution system using deep belief network with non-Gaussian uncertaintiesY Huang, Q Xu, C Hu, Y Sun, G LinIEEE Sensors Journal 19 (20), 9422-9430

2018

  1. Local feature sufficiency exploration for predicting security-constrained generation dispatch in multi-area power systemsY Sun, X Fan, Q Huang, X Li, R Huang, T Yin, G Lin2018 17th IEEE International Conference on Machine Learning and Applications
  2. Deep neural network regression and sobol sensitivity analysis for daily solar energy prediction given weather dataY SunPurdue University