Search Results for author: Peter Y. Lu

Found 6 papers, 5 papers with code

Discovering Conservation Laws using Optimal Transport and Manifold Learning

1 code implementation31 Aug 2022 Peter Y. Lu, Rumen Dangovski, Marin Soljačić

Using tools from optimal transport theory and manifold learning, our proposed method provides a direct geometric approach to identifying conservation laws that is both robust and interpretable without requiring an explicit model of the system nor accurate time information.

Deep Learning and Symbolic Regression for Discovering Parametric Equations

no code implementations1 Jul 2022 Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljačić

Symbolic regression is a machine learning technique that can learn the governing formulas of data and thus has the potential to transform scientific discovery.

BIG-bench Machine Learning regression +1

Discovering Sparse Interpretable Dynamics from Partial Observations

1 code implementation22 Jul 2021 Peter Y. Lu, Joan Ariño, Marin Soljačić

Identifying the governing equations of a nonlinear dynamical system is key to both understanding the physical features of the system and constructing an accurate model of the dynamics that generalizes well beyond the available data.

Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure

1 code implementation23 Apr 2021 Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić

Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box.

Gaussian Processes

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

1 code implementation10 Dec 2019 Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Čeperić, Marin Soljačić

We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared to a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery.

Explainable Models regression +1

Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning

1 code implementation13 Jul 2019 Peter Y. Lu, Samuel Kim, Marin Soljačić

Our method for discovering interpretable latent parameters in spatiotemporal systems will allow us to better analyze and understand real-world phenomena and datasets, which often have unknown and uncontrolled variables that alter the system dynamics and cause varying behaviors that are difficult to disentangle.

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