no code implementations • 30 Nov 2023 • Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Samuel Kim, Peter Y. Lu, Thomas Christensen, Marin Soljačić
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials.
1 code implementation • NeurIPS 2023 • Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett
In this paper, we propose an alternative framework designed to preserve invariant measures of chaotic attractors that characterize the time-invariant statistical properties of the dynamics.
2 code implementations • 31 May 2023 • Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models.
no code implementations • 20 Mar 2023 • Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic
Model stitching (Lenc & Vedaldi 2015) is a compelling methodology to compare different neural network representations, because it allows us to measure to what degree they may be interchanged.
no code implementations • 23 Feb 2023 • Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljačić
Studying the dynamics of open quantum systems can enable breakthroughs both in fundamental physics and applications to quantum engineering and quantum computation.
1 code implementation • 31 Aug 2022 • Peter Y. Lu, Rumen Dangovski, Marin Soljačić
We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values.
1 code implementation • 1 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.
1 code implementation • 22 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.
2 code implementations • 23 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.
1 code implementation • 10 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.
1 code implementation • 13 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.