Scientific Computing with Neural Surrogates: Hype or Hope? Data Science Department, IISER Pune, India (July 3, 2025)
Rethinking Scientific Computing: Are Neural Surrogates the Real Deal?, Mathematics Department, VNIT Nagpur, India (June 30, 2025)
Aerospace and Mechanical Engineering Seminars, WPI, USA. (August and Sept. 2024)
Mathematical Sciences Departmental Colloquium, WPI, USA. (April 12, 2024).
Physics-Informed Neural networks and Neural Operator Networks: Methods and Applications, Oak Ridge National Laboratory, USA. (February 8, 2024) (Flyer)
Phi-ML meets Engineering, The Alan Turing Institute, UK. (February 1, 2024) (Flyer)
A physics-informed neural network-based solution to inverse problems in high-speed fluid flows, International Symposium on Recent Trends in Numerical Methods, IIT Kanpur, India. (January 21, 2024)
Physics-Informed Deep Learning: Methods and Applications in Scientific Computing, Worcester Polytechnic Institute, USA. (January 10, 2024)
Physics-Driven Deep Learning Methods for Scientific Computing, 5th International Conference on Mathematical Techniques and Applications (ICMTA-2024), SRM, India. (January 3, 2024)
Physics-Informed Deep learning: Merging Data with Physics, Shell .ai, Aryabhata Series, Shell Technology Center Bangalore, India, Oct. 20, 2023 (Flyer)
Short Course on Physics-Informed Deep Learning at TIFR-CAM, Bengaluru, India (18 Oct. 2023) [Link]
Scientific Machine Learning through the Lens of Physics-Informed Neural Networks, Lawrence Livermore National Laboratory, USA, April 14, 2023.
Scientific Machine Learning through the Lens of Physics-Informed Neural Networks, SankhyaSutra Labs, India, Feb. 24, 2023. (Flyer)
Physics-Informed Neural Networks for Inverse Problems in Supersonic Flows, Theoretical Division, Los Alamos National Laboratory, USA Oct. 24, 2022
Artificial Neural Networks for scientific computations: Embedding physics and data, The University of Texas at El Paso (UTEP), USA, Oct. 21, 2022.
Physics-Informed Neural Networks for Scientific Computations: Algorithms and Applications, BIMSA-Tsinghua seminar on Machine Learning and Differential Equations, Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, and Tsinghua University, China, Oct. 12, 2022. (Flyer)
Physics-Informed Machine Learning: Merging Data and Physics, High-Performance Computing and AI Predictive Tools in Fluids and Thermal, NIT Rourkela, India, July 26, 2022.
Physics-Informed Machine Learning for scientific computations: Recent Advances and Applications, Theoretical Division, Los Alamos National Laboratory, USA, May 20, 2022.
Physics-Informed Neural Networks: A new paradigm for learning physical laws, Conference on PDE and Numerical Analysis, Tata Institute of Fundamental Research - Center for Applicable Mathematics, Bengaluru, India, April 30, 2022. (Link)
Physics-Informed Machine Learning for scientific computations: Recent Advances and Applications, LANS Seminar, Argonne National Laboratory, USA, February 23, 2022. (Flyer)
Physics-Informed Machine Learning for scientific computations, ECE Colloquium Series, University of Connecticut, USA, November 19, 2021.
Scientific Machine Learning: From PINNs to eXtended PINNs (XPINNs), SRM University, India, September 11, 2021. (Flyer)
A Generalized Space-Time Domain Decomposition based Extended Physics-Informed Neural Networks for partial differential equations: Method and Implementation, Carnegie Mellon University, USA, June 17, 2021.
Parallel Physics-Informed Neural Networks via Domain Decomposition, Seminars of the Interdisciplinary Area of Computational Engineering and Science, COPPE/Federal University of Rio de Janeiro, Brazil, June 10, 2021. (Poster)
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations, AI Chair OceaniX Webinars 2021, IMT Atlantique, Brest, France, March 24, 2021.
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations, AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning in Physics Sciences, Stanford University, Palo Alto, California, USA, March 22, 2021.
Hyperbolic Conservation Laws: General Introduction and Numerical Solution, Instructional School for Teachers on “ PDE: Theory and Computation”, Department of Mathematics, Indian Institute of Science, Bengaluru, India (August 4, 2018).
Higher Order Spectral Method of Relaxed Streamlined-Upwinding for Nonlinear Conservation Law, Computational Science Symposium (CSS 2017), Centre for Data Science, Indian Institute of Science, Bengaluru. India (March 18, 2017).
Stabilized Finite Element Schemes for Hyperbolic Conservation Laws, Tata Institute of Fundamental Research - Center for Applicable Mathematics, Bengaluru, India (August 18, 2016).
Mathematical Modeling and Computer Simulation of Physical Systems, St. Vincent Palloti Engineering College, Nagpur, India (Sept. 24, 2015).
Differential Equations for Aerospace Engineers, Priyadarshani College of Engineering, Nagpur, India (Dec. 30, 2013).
Level-Set Based eXtended FEM method, Institute fur Statik, Technical University of Braunschweig, Germany (April 26, 2012).