Christian Moya
Physics and Astronomy · Purdue University West Lafayette
Publications
59
Citations
502
Est. group size
~4
Recurring co-author estimate
Active years
14
Publishing since 2013
Christian Moya works on machine learning methods for scientific computing and engineering, with a particular focus on 'operator learning' — training neural networks (such as DeepONet architectures) to approximate solutions of differential equations and simulate complex physical systems. Much of the recent work applies these methods to power grid dynamics, multiscale physics problems, and improving the training and reliability (uncertainty quantification) of these neural network models.
Publication output grew markedly from 2021 to a peak in 2023, and has remained at a comparatively high and steady level (around 8 per year) through 2024-2025.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- FPINN-deeponet: A physics-informed operator learning framework for multi-term time-fractional mixed diffusion-wave equations
Journal of Computational Physics · 2025
- DeepONet as a multi-Operator extrapolation model: Distributed pretraining with physics-Informed fine-Tuning
Journal of Computational Physics · 2025
- A self-adaptive energy-based learning rate for stochastic gradient descent via Vector Auxiliary Variable method
Engineering Applications of Artificial Intelligence · 2025
- Deeponet as a Multi-Operator Extrapolation Model Distributed Pretraining with Physics-Informed Fine-Tuning
SSRN Electronic Journal · 2025
- Fpinn-Deeponet: An Operator Learning Framework for Multi-Term Time-Fractional Mixed Diffusion-Wave Equations
SSRN Electronic Journal · 2025
- An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with Vector Auxiliary Variable Method
SSRN Electronic Journal · 2025
- D2NO: Efficient handling of heterogeneous input function spaces with distributed deep neural operators
Computer Methods in Applied Mechanics and Engineering · 2024
- Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE
Multiscale Modeling and Simulation · 2024
- Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
arXiv (Cornell University) · 2024
- An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method
arXiv (Cornell University) · 2024
- DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning
arXiv (Cornell University) · 2024
- DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories
Neurocomputing · 2023
- Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks
Engineering Applications of Artificial Intelligence · 2023
- NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training
Algorithms · 2023
- DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems
IEEE Systems Journal · 2023
- arXiv (Cornell University)×22
- SSRN Electronic Journal×5
- Journal of Computational Physics×3
- Engineering Applications of Artificial Intelligence×2
- Algorithms×2
- Yuezhu Xu
Physics and Astronomy · Purdue University West Lafayette
- Debdipta Goswami
Physics and Astronomy · The Ohio State University
- Guang Lin
Physics and Astronomy · Purdue University West Lafayette
- Naxian Ni
Physics and Astronomy · Purdue University West Lafayette
- Xihaier Luo
Physics and Astronomy · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile