4 papers
TB-Transolver: Parallelized and Coherent Long-Horizon Rollouts via Temporal Bundling for Time-Dependent PDEs
Sidharth S. Menon, Ameya D. Jagtap
Transolver has recently gained widespread adoption for solving diverse physical problems on unstructured geometric meshes by leveraging its Physics-Attention mechanism, which seaml…
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
Sidharth S. Menon, Irina Tezaur, Ameya D. Jagtap
Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability while solving partial differen…
BubbleOKAN: A Physics-Informed Interpretable Neural Operator for High-Frequency Bubble Dynamics
Yunhao Zhang, Sidharth S. Menon, Lin Cheng +2
In this work, we employ physics-informed neural operators to map pressure profiles from an input function space to the corresponding bubble radius responses. Our approach employs a…
Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs
Sidharth S. Menon, Ameya D. Jagtap
High-dimensional partial differential equations (PDEs) arise in diverse scientific and engineering applications but remain computationally intractable due to the curse of dimension…