Guang Lin
Physics and Astronomy · Purdue University West Lafayette
Publications
402
Citations
6,671
Est. group size
~20
Recurring co-author estimate
Active years
26
Publishing since 2001
Guang Lin works on developing mathematical and machine learning methods for modeling complex physical and biological systems, combining tools like physics-informed neural networks, diffusion models, and Bayesian/Kalman filtering techniques. Application areas in recent work span protein structure prediction, PDE (partial differential equation) solvers, image and signal processing, EEG-based emotion recognition, and large language model reasoning and safety. This is a broad, methods-driven research program bridging computational mathematics, scientific machine learning, and applied data science.
Publication output has grown substantially over the last decade, rising from around 14 papers in 2017 to over 50 in 2025, with a generally steady increase and no signs of slowing.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- LegONet: Plug-and-Play Structure-Preserving Neural Operator Blocks for Compositional PDE Learning
Research Square · 2026
- A Hyperbolic Neural Closure for M1 Radiation Transfer
SSRN Electronic Journal · 2026
- ExEnDiff: An Experiment-Guided Diffusion Model for Protein Conformational Ensemble Generation
PRX Life · 2025
- Adversarial guided diffusion models for adversarial purification
Neural Networks · 2025
- High-quality three-dimensional cartoon avatar reconstruction with Gaussian splatting
Engineering Applications of Artificial Intelligence · 2025
- iPINNER: An iterative physics-informed neural network with ensemble Kalman filter
Journal of Computational Physics · 2025
- Conditional probabilistic-based domain adaptation for cross-subject EEG-based emotion recognition
Cognitive Neurodynamics · 2025
- PREDICTING POTENTIAL DISTRIBUTION OF CUCURBIT LEAF BEETLES (AULACOPHORA INDICA AND AULACOPHORA LEWISII) IN CHINA USING MAXIMUM ENTROPY MODELING
Applied Ecology and Environmental Research · 2025
- Coefficient-to-Basis Network: a fine-tunable operator learning framework for inverse problems with adaptive discretizations and theoretical guarantees
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2025
- Hyperspectral Pansharpening via Diffusion Models with Iteratively Zero-Shot Guidance
2025
- Energy-Dissipative Evolutionary Kolmogorov–Arnold Networks for Complex Pde Systems
SSRN Electronic Journal · 2025
- ProTDyn: a foundation Protein language model for Thermodynamics and Dynamics generation
bioRxiv (Cold Spring Harbor Laboratory) · 2025
- LLM Safety Alignment is Divergence Estimation in Disguise
arXiv (Cornell University) · 2025
- LLM Reasoning Engine: Specialized Training for Enhanced Mathematical Reasoning
2025
- Turbulence Deconvolution Using Optimization on Quotient Manifold
SSRN Electronic Journal · 2025
- arXiv (Cornell University)×80
- Journal of Computational Physics×32
- SSRN Electronic Journal×20
- Journal of Computational and Applied Mathematics×8
- Water Resources Research×4
- Min Liu
Physics and Astronomy · Purdue University West Lafayette
- Christian Moya
Physics and Astronomy · Purdue University West Lafayette
- Guang Lin
Physics and Astronomy · Purdue University West Lafayette
- Amirhossein Mollaali
Physics and Astronomy · Purdue University West Lafayette
- Adithya Srinivasan
Physics and Astronomy · Purdue University West Lafayette
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