no code implementations • 20 Nov 2023 • Jan Hückelheim, Tadbhagya Kumar, Krishnan Raghavan, Pinaki Pal
Computational Fluid Dynamics (CFD) is used in the design and optimization of gas turbines and many other industrial/ scientific applications.
no code implementations • 2 Oct 2023 • Hongwei Jin, Krishnan Raghavan, George Papadimitriou, Cong Wang, Anirban Mandal, Ewa Deelman, Prasanna Balaprakash
To address this problem, we introduce an autoencoder-driven self-supervised learning~(SSL) approach that learns a summary statistic from unlabeled workflow data and estimates the normal behavior of the computational workflow in the latent space.
no code implementations • 19 May 2023 • Krishnan Raghavan, Prasanna Balaprakash
However, the literature is quite sparse, when the data corresponding to a CL task is nonEuclidean-- data , such as graphs, point clouds or manifold, where the notion of similarity in the sense of Euclidean metric does not hold.
no code implementations • 20 Feb 2023 • Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash
We demonstrate the feasibility of this framework for two tasks - forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature.
no code implementations • 28 Oct 2021 • Krishnan Raghavan, Vignesh Narayanan, Jagannathan Sarangapani
In this paper, we address two key challenges in deep reinforcement learning setting, sample inefficiency and slow learning, with a dual NN-driven learning approach.
no code implementations • 28 Oct 2021 • Krishnan Raghavan, Vignesh Narayanan, Jagannathan Saraangapani
Learning to control complex systems using non-traditional feedback, e. g., in the form of snapshot images, is an important task encountered in diverse domains such as robotics, neuroscience, and biology (cellular systems).
no code implementations • 26 Oct 2021 • Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash
However, building ensembles of neural networks is a challenging task because, in addition to choosing the right neural architecture or hyperparameters for each member of the ensemble, there is an added cost of training each model.
no code implementations • NeurIPS 2021 • Krishnan Raghavan, Prasanna Balaprakash
We formulate the continual learning (CL) problem via dynamic programming and model the trade-off between catastrophic forgetting and generalization as a two-player sequential game.
no code implementations • 1 Jan 2021 • Krishnan Raghavan, Prasanna Balaprakash
Meta continual learning algorithms seek to train a model when faced with similar tasks observed in a sequential manner.