Barajas-Solano, David
- Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
2018/08/10 by Tartakovsky, Alexandre M., Marrero, Carlos Ortiz, Perdikaris, Paris +2 · 3 citations
#Analysis of PDEs (math.AP) #Computational Physics (physics.comp-ph) #FOS: Mathematics #FOS: Physical sciences
- Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
2019/10/29 by Liu Yang, Yang, Liu, Sean Treichler +18 · 5 citations
Physics and Astronomy · Decision Sciences · Computer Science · #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design #Gaussian Processes and Bayesian Inference
- Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach
2022/12/10 by S. Sinha, Sai Pushpak Nandanoori, Sinha, S. +3 · 2 citations
Computer Science · Engineering · Physics and Astronomy · #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Power System Optimization and Stability
- Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems
2023/12/11 by Yifei Zong, Zong, Yifei, David A. Barajas‐Solano +3 · 1 citation
Computer Science · Materials Science · Physics and Astronomy · #60H15 #60J10 #68T07 #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning in Materials Science #Model Reduction and Neural Networks
- Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation
2024/07/05 by Yifei Zong, David A. Barajas‐Solano, Zong, Yifei +3 · 2 citations
Computer Science · Earth and Planetary Sciences · Environmental Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations