5 papers
Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery
Amirhossein Nouranizadeh, Sarang Rajendra Patil, Alan John Varghese +3
Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse problems, but their training ty…
PI-SONet: A Physics-Informed Symplectic Operator Network for Real-Time Optimal Control of Multi-Agent Systems
Alan John Varghese, Shanqing Liu, Paula Chen +3
Many real-life applications involve controlling high-dimensional multi-agent systems in real-time. Existing optimal control solvers often suffer from the curse-of-dimensionality an…
Automatic selection of the best neural architecture for time series forecasting
Qianying Cao, Shanqing Liu, Alan John Varghese +3
Time series forecasting plays a pivotal role in a wide range of applications, including weather prediction, healthcare, structural health monitoring, predictive maintenance, energy…
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Ashish Parmanand Pandey, Alan John Varghese, Sarang Patil +1
Dynamic graph embedding has emerged as an important technique for modeling complex time-evolving networks across diverse domains. While transformer-based models have shown promise…
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
Juan Diego Toscano, Vivek Oommen, Alan John Varghese +4
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and…