Conferences


VarMiON: Variationally Mimetic Operator Network. [slides]

ECCOMAS
Lisboa, Portugal, June 2024

Deep learning-based high-order entropy stable schemes for conservation laws. [slides]

ECCOMAS
Lisboa, Portugal, June 2024

A variationally mimetic operator network . [slides]

AMS Eastern Sectional Meeting
Howard University, Washington DC, April 2024

A variationally mimetic operator network . [slides]

AMS Southeastern Sectional Meeting
Florida State University, Tallahassee, March 2024

Novel Conditional Wasserstein GAN for Bayesian Inference. [slides]

SIAM-UQ 2024
Triest, Italy, February 2024

Learning WENO for entropy stable schemes to solve conservation laws. [slides]

Brin Workshop in Scientific Machine Learning
University of Maryland, February 2024

A deep learning strategy for solving physics-based Bayesian inference problems [slides]

Data Science Symposium
South Dakota State University, February 2024

A variationally mimetic operator network [slides]

5th International Conference on Mathematical Techniques and Applications
SRM Institute of Science and Technology, Kattankulathur (India), 3rd January, 2024

A variationally mimetic operator network [slides]

USACM Workshop on Establishing Benchmarks for Data-Driven Modeling of Physical Systems
University of Southern California, Los Angeles, 6th April, 2023

Conditional GANs and their generalizability in physics-based inverse problems [slides]

USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling
Arlington, Virginia, 18th August, 2022

Deep leaning-based posterior inference for inverse problems [slides]

Conference on PDE and numerical analysis
TIFR-CAM, Bangalore, 28th April, 2022

A data-driven approach to predict artificial viscosity in high-order solvers [slides]

AMS Spring Central Sectional Meeting, March 2022

Bayesian inference using generative adversarial networks [slides]

87th Annual Conference of the Indian Mathematical Society (Invited Talk in Minisymposium)
Aurangabad, 7th December, 2021

Discontinuous Galerkin discretization of phase-field models for pore-scale flows [slides]

SIAM-GS 2021 (Invited Talk in Minisymposium)
Milan, Italy, 24th June, 2021

A Deep Learning Framework for p-adaptation [slides]

SIAM-CSE 2021 (Talk in Minisymposium)
Fort Worth, Texas, 5th March, 2021

Using deep learning to overcome algorithmic bottlenecks [slides]

Numerical methods for hyperbolic problems (NumHyp-2019) (Invited Speaker)
Malaga, Spain, 18th June, 2019

Controlling oscillations in high-order accurate methods through artificial neural networks [slides]

SIAM-CSE 2019 (Invited Talk in Minisymposium)
Spokane, 28th February, 2019

A fully-discrete kinetic energy preserving and entropy conservative scheme for compressible flows [slides]

SIAM-CSE 2019 (Invited Talk in Minisymposium)
Spokane, 27th February, 2019

An artificial neural network as a troubled-cell indicator [slides]

SIAM Annual Meeting - 2018 (Invited Talk in Minisymposium)
Portland, 10th July, 2018

An artificial neural network for detecting discontinuities [slides]

7th International Conference on High Performance Scientific Computing (Invited Talk in Minisymposium)
Hanoi, 11th March, 2018

A high-resolution energy preserving method for the rotating shallow water equation [slides]

ENUMATH - 2017 (Invited Talk in Minisymposium)
Voss, 27th September, 2017

A third order entropy stable scheme for the compressible Euler equations [slides]

XVI International Conference on Hyperbolic Problems (HYP2016)
Aachen, 4th August, 2016

A sign preserving WENO reconstruction [slides]

ICIAM - 2015
Beijing, 14th August, 2015

Entropy stable schemes for compressible flows on unstructured meshes

Conference on Computational PDEs, Finite Element Meet, TIFR-CAM
Bangalore, 20th December, 2014

Entropy stable schemes for compressible flows on unstructured meshes [slides]

The 5th International Conference on Scientific Computing and Partial Differential Equations, HKBU
Hong Kong, 9th November, 2014



Seminars and Colloquia


Learning WENO for entropy stable schemes to solve conservation laws. [slides]

Ohio State University, 12th April, 2024

A deep learning strategy for solving physics-based Bayesian inference problems [slides]

CMAI, George Mason University, 16th February, 2024

A deep learning strategy for solving physics-based Bayesian inference problems [slides]

Department of Mathematics, Brigham Young University, 13th October, 2023

A variationally mimetic operator network

Department of Mathematics, Iowa State University, 17th April, 2023

Bayesian inference using GANS [slides]

RIT: ML for Rare Events, UMD, 31st March, 2023

A variationally mimetic operator network

Department of Mathematics, North Carolina State University, 21st March, 2023

A variationally mimetic operator network [slides]

LANS seminar
Argonne National Laboratory, 25th January, 2023

A variationally mimetic operator network [slides]

CMX Seminar
Caltech, 16th November, 2022

Deep leaning aided Bayesian inference [slides]

REU Exposure Seminar
University of Maryland, 27th July, 2022

Deep leaning-based posterior inference for inverse problems [slides]

Annual Math Symposium
IISER Bhopal, 26th March, 2022

Solving physics-based inverse problems using generative adversarial networks [slides]

Department of Mathematics and Statistics, University of North Carolina at Charlotte; 8th October, 2021.

A data-driven approach to predict artificial viscosity in high-order solvers [slides]

Department of Mathematics, University of Wurzburg, Germany; 14th May, 2021.

Data-driven enhancements of numerical methods [slides]

Department of Mathematical Sciences, Michigan Technological University; 2nd March, 2020.

Deep learning enhancements of numerical methods

Department of Mathematics, University of Florida; 12th February, 2020.

Deep learning enhancements of numerical methods [slides]

CAAM Colloquium, Rice University; 9th September, 2019.

Controlling spurious oscillations in high-order methods through deep neural networks [slides]

TIFR-CAM; 9th January, 2019.

Controlling spurious oscillations in high-order methods through deep neural networks [slides]

High-Fidelity Industrial LES/DNS symposium, Brussels; 15th November, 2018.

Using neural nets to detect discontinuities

MATHICSE Retreat, St. Croix; 19th June, 2018.

An artificial neural network for detecting discontinuities [slides]

TIFR-CAM; 4th January, 2018.

A sign preserving WENO reconstruction

Department of Mathematics, University of Wurzburg; 23rd November, 2015.

A sign preserving WENO reconstruction

Department of Applied Mathematics, University of Washington; 11th June, 2015.

Entropy stable schemes for compressible flows

Department of Mathematics, University of Wurzburg; 9th July, 2014.



Posters


Bayesian Inference in Physics-Driven Problems with Adversarial Priors [poster]

NeurIPS Workshop on Deep Learning and Inverse Problems, 2020.

Entropy stable schemes for compressible flows on unstructured meshes [poster]

Workshop on the Analysis and Numerical Approximation of PDEs, ETH Zurich; 9th July 2014.