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Found 5 papers, 1 papers with code
Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
To perform future forecasting of pressure and velocity on the challenging EAGLE dataset, we introduce a new mesh transformer.
Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach
The identification of a nonlinear dynamic model is an open topic in control theory, especially from sparse input-output measurements.
Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data.
Deep KKL: Data-driven Output Prediction for Non-Linear Systems
Building on this formulation and problem definition, we propose a predictor structure based on the Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well into our general framework.
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks
Temperature Scaling (TS) is a state-of-the-art among measure-based calibration methods which has low time and memory complexity as well as effectiveness.
