An Improved Time-of-Arrival Estimation for WLAN-Based Local Positioning. - Harish Reddy; M. Girish Chandra; P. Balamuralidhar; Harihara S. G.; Kaushik Bhattacharya; Edward Joseph (COMSWARE, 2007)

A mesh-free convex approximation scheme for Kohn-Sham density functional theory. - Phanish Suryanarayana; Kaushik Bhattacharya; Michael Ortiz (J. Comput. Phys., 2011)

Stability of MultiComponent Biological Membranes. - Sefi Givli; Ha Giang; Kaushik Bhattacharya (SIAM J. Appl. Math., 2012)

Phase-field study of crack nucleation and propagation in elastic - perfectly plastic bodies. - Stella Brach; Erwan Tanné; Blaise Bourdin; Kaushik Bhattacharya (CoRR, 2018)

Multipole Graph Neural Operator for Parametric Partial Differential Equations. - Zongyi Li; Nikola B. Kovachki; Kamyar Azizzadenesheli; Burigede Liu; Andrew M. Stuart; Kaushik Bhattacharya; Anima Anandkumar (NeurIPS, 2020)

Neural Operator: Graph Kernel Network for Partial Differential Equations. - Zongyi Li; Nikola B. Kovachki; Kamyar Azizzadenesheli; Burigede Liu; Kaushik Bhattacharya; Andrew M. Stuart; Anima Anandkumar (CoRR, 2020)

Model Reduction and Neural Networks for Parametric PDEs. - Kaushik Bhattacharya; Bamdad Hosseini; Nikola B. Kovachki; Andrew M. Stuart (CoRR, 2020)

Accelerated computational micromechanics. - Hao Zhou; Kaushik Bhattacharya (CoRR, 2020)

Large scale ab-initio simulations of dislocations. - Mauricio Ponga; Kaushik Bhattacharya; Michael Ortiz (J. Comput. Phys., 2020)

Fourier Neural Operator for Parametric Partial Differential Equations. - Zongyi Li; Nikola Borislavov Kovachki; Kamyar Azizzadenesheli; Burigede Liu; Kaushik Bhattacharya; Andrew M. Stuart; Anima Anandkumar (ICLR, 2021)

Markov Neural Operators for Learning Chaotic Systems. - Zongyi Li; Nikola B. Kovachki; Kamyar Azizzadenesheli; Burigede Liu; Kaushik Bhattacharya; Andrew M. Stuart; Anima Anandkumar (CoRR, 2021)

Neural Operator: Learning Maps Between Function Spaces. - Nikola B. Kovachki; Zongyi Li; Burigede Liu; Kamyar Azizzadenesheli; Kaushik Bhattacharya; Andrew M. Stuart; Anima Anandkumar (CoRR, 2021)

Learning Chaotic Dynamics in Dissipative Systems. - Zongyi Li; Miguel Liu-Schiaffini; Nikola B. Kovachki; Kamyar Azizzadenesheli; Burigede Liu; Kaushik Bhattacharya; Andrew M. Stuart; Anima Anandkumar (NeurIPS, 2022)

Spectral quadrature for the first principles study of crystal defects: Application to magnesium. - Swarnava Ghosh; Kaushik Bhattacharya (J. Comput. Phys., 2022)

Learning Dissipative Dynamics in Chaotic Systems (Datasets). - Zongyi Li; Miguel Liu-Schiaffini; Nikola Borislavov Kovachki; Burigede Liu; Kamyar Azizzadenesheli; Kaushik Bhattacharya; Andrew M. Stuart; Anima Anandkumar (Zenodo, 2022)

A learning-based multiscale model for reactive flow in porous media. - Mina Karimi; Kaushik Bhattacharya (CoRR, 2023)

Accelerated Computational Micromechanics for Reactive Flow in Porous Media. - Mina Karimi; Kaushik Bhattacharya (CoRR, 2023)

Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs. - Nikola B. Kovachki; Zongyi Li; Burigede Liu; Kamyar Azizzadenesheli; Kaushik Bhattacharya; Andrew M. Stuart; Anima Anandkumar (J. Mach. Learn. Res., 2023)

Learning Markovian Homogenized Models in Viscoelasticity. - Kaushik Bhattacharya; Burigede Liu; Andrew M. Stuart; Margaret Trautner (Multiscale Model. Simul., 2023)

Systematic design of compliant morphing structures: a phase-field approach. - Jamal Shabani; Kaushik Bhattacharya; Blaise Bourdin (CoRR, 2024)

Learning Homogenization for Elliptic Operators. - Kaushik Bhattacharya; Nikola B. Kovachki; Aakila Rajan; Andrew M. Stuart; Margaret Trautner (SIAM J. Numer. Anal., 2024)

Learning Memory and Material Dependent Constitutive Laws. - Kaushik Bhattacharya; Lianghao Cao; George Stepaniants; Andrew M. Stuart; Margaret Trautner (CoRR, 2025)