Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids. - Nikhil Muralidhar; Jie Bu; Ze Cao; Long He; Naren Ramakrishnan; Danesh K. Tafti; Anuj Karpatne (CoRR, 2019)

PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly. - Nikhil Muralidhar; Jie Bu; Ze Cao; Long He; Naren Ramakrishnan; Danesh K. Tafti; Anuj Karpatne (SDM, 2020)

Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems. - Nikhil Muralidhar; Jie Bu; Ze Cao; Long He; Naren Ramakrishnan; Danesh K. Tafti; Anuj Karpatne (Big Data, 2020)

Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics. - Jie Bu; Mohannad Elhamod; Christopher Singh; Matthew Redell; Wei-Cheng Lee; Anuj Karpatne (CoRR, 2020)

Beyond Observed Connections : Link Injection. - Jie Bu; M. Maruf; Arka Daw (CoRR, 2020)

Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems. - Mohannad Elhamod; Jie Bu; Christopher Singh; Matthew Redell; Abantika Ghosh; Viktor Podolskiy; Wei-Cheng Lee; Anuj Karpatne (AAAI Spring Symposium: MLPS, 2021)

PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields. - Nikhil Muralidhar; Jie Bu; Ze Cao; Neil Raj; Naren Ramakrishnan; Danesh K. Tafti; Anuj Karpatne (ICDM, 2021)

Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM). - Jie Bu; Arka Daw; M. Maruf; Anuj Karpatne (NeurIPS, 2021)

Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs. - Jie Bu; Anuj Karpatne (SDM, 2021)

Rethinking the Importance of Sampling in Physics-informed Neural Networks. - Arka Daw; Jie Bu; Sifan Wang; Paris Perdikaris; Anuj Karpatne (CoRR, 2022)

Robust multi-view subspace clustering based on consensus representation and orthogonal diversity. - Nan Zhao 003; Jie Bu (Neural Networks, 2022)

CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems. - Mohannad Elhamod; Jie Bu; Christopher Singh; Matthew Redell; Abantika Ghosh; Viktor Podolskiy; Wei-Cheng Lee; Anuj Karpatne (ACM Trans. Intell. Syst. Technol., 2022)

Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. - Arka Daw; Jie Bu; Sifan Wang; Paris Perdikaris; Anuj Karpatne (ICML, 2023)

Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation. - Jie Bu; Kazi Sajeed Mehrab; Anuj Karpatne (CoRR, 2023)

Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation. - M. Maruf; Arka Daw; Amartya Dutta; Jie Bu; Anuj Karpatne (CoRR, 2023)

Achieving More with Less: Learning Generalizable Neural Networks With Less Labeled Data and Computational Overheads. - Jie Bu (2023)