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Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model that is predictive…

Machine Learning · Statistics 2021-01-18 Sebastian Kaltenbach , Phaedon-Stelios Koutsourelakis

We apply reinforcement learning (RL) to robotics tasks. One of the drawbacks of traditional RL algorithms has been their poor sample efficiency. One approach to improve the sample efficiency is model-based RL. In our model-based RL…

Machine Learning · Computer Science 2023-05-16 Adithya Ramesh , Balaraman Ravindran

Accuracy and generalization capabilities are key objectives when learning dynamical system models. To obtain such models from limited data, current works exploit prior knowledge and assumptions about the system. However, the fusion of…

Machine Learning · Statistics 2025-08-22 Björn Volkmann , Jan-Hendrik Ewering , Michael Meindl , Simon F. G. Ehlers , Thomas Seel

Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation. We survey the rapidly…

Machine Learning · Computer Science 2021-09-17 Bharath Ramsundar , Dilip Krishnamurthy , Venkatasubramanian Viswanathan

Physics-informed neural networks allow models to be trained by physical laws described by general nonlinear partial differential equations. However, traditional architectures struggle to solve more challenging time-dependent problems due to…

Computational Physics · Physics 2022-02-28 Benjamin Wu , Oliver Hennigh , Jan Kautz , Sanjay Choudhry , Wonmin Byeon

Mechanistic models with differential equations are a key component of scientific applications of machine learning. Inference in such models is usually computationally demanding, because it involves repeatedly solving the differential…

Machine Learning · Statistics 2022-07-06 Jonathan Schmidt , Nicholas Krämer , Philipp Hennig

Learning precise surrogate models of complex computer simulations and physical machines often require long-lasting or expensive experiments. Furthermore, the modeled physical dependencies exhibit nonlinear and nonstationary behavior.…

Machine Learning · Computer Science 2023-03-20 Matthias Bitzer , Mona Meister , Christoph Zimmer

In spatially extended systems, it is common to find latent variables that are hard, or even impossible, to measure with acceptable precision, but are crucially important for the proper description of the dynamics. This substantially…

Numerical Analysis · Computer Science 2019-08-28 Patrick A. K. Reinbold , Roman O. Grigoriev

Differential equations are important mechanistic models that are integral to many scientific and engineering applications. With the abundance of available data there has been a growing interest in data-driven physics-informed models.…

Machine Learning · Computer Science 2025-02-04 Oliver Hamelijnck , Arno Solin , Theodoros Damoulas

Model-free data-driven computational mechanics replaces phenomenological constitutive functions by numerical simulations based on data sets of representative samples in stress-strain space. The distance of strain and stress pairs from the…

Computational Engineering, Finance, and Science · Computer Science 2021-11-29 Kerem Ciftci , Klaus Hackl

Scientific measurements are often bottlenecked by suboptimal conditions, whether that be noise, incomplete spatial coverage, or limited resolution, rendering accurate field reconstruction a difficult task. We introduce LatentPDE, a latent…

Machine Learning · Computer Science 2026-04-28 Valerie Tsao , Nathaniel Chaney , Manolis Veveakis

We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential…

Computational Physics · Physics 2023-08-21 Shawn G. Rosofsky , Hani Al Majed , E. A. Huerta

This paper presents a probabilistic approach to represent and quantify model-form uncertainties in the reduced-order modeling of complex systems using operator inference techniques. Such uncertainties can arise in the selection of an…

Machine Learning · Statistics 2024-11-08 Jin Yi Yong , Rudy Geelen , Johann Guilleminot

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning…

Extensions and improvements of empirical force fields are discussed in view of applications to computational vibrational spectroscopy and reactive molecular dynamics simulations. Particular focus is on quantitative studies which make…

Chemical Physics · Physics 2020-07-08 D. Koner , M. S. Salehi , P. Mondal , M. Meuwly

A lattice model with a spatial dispersion corresponding to a power-law type is suggested. This model serves as a microscopic model for elastic continuum with power-law non-locality. We prove that the continuous limit maps of the equations…

Mathematical Physics · Physics 2015-01-07 Vasily E. Tarasov

We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct…

Machine Learning · Statistics 2019-06-26 Yibo Yang , Paris Perdikaris

Model-order reduction techniques allow the construction of low-dimensional surrogate models that can accelerate engineering design processes. Often, these techniques are intrusive, meaning that they require direct access to underlying…

Dynamical Systems · Mathematics 2023-08-16 Yevgeniya Filanova , Igor Pontes Duff , Pawan Goyal , Peter Benner

A core problem in machine learning is to learn expressive latent variables for model prediction on complex data that involves multiple sub-components in a flexible and interpretable fashion. Here, we develop an approach that improves…

Machine Learning · Computer Science 2024-02-13 Yi-Lin Tuan , Zih-Yun Chiu , William Yang Wang

Data-driven models for predicting dynamic responses of linear and nonlinear systems are of great importance due to their wide application from probabilistic analysis to inverse problems such as system identification and damage diagnosis. In…

Machine Learning · Computer Science 2020-12-29 Soheil Sadeghi Eshkevari , Martin Takáč , Shamim N. Pakzad , Majid Jahani
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