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Found 9 papers, 0 papers with code
Forward Gradients for Data-Driven CFD Wall Modeling
Computational Fluid Dynamics (CFD) is used in the design and optimization of gas turbines and many other industrial/ scientific applications.
Self-supervised Learning for Anomaly Detection in Computational Workflows
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.
Learning Continually on a Sequence of Graphs -- The Dynamical System Way
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.
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
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.
Cooperative Deep $Q$-learning Framework for Environments Providing Image Feedback
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.
Learning to Control using Image Feedback
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).
AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification
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.
Formalizing the Generalization-Forgetting Trade-off in Continual Learning
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.
Meta-Continual Learning Via Dynamic Programming
Meta continual learning algorithms seek to train a model when faced with similar tasks observed in a sequential manner.
