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Related papers: Inferring Global Dynamics Using a Learning Machine

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Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for…

Chemical Physics · Physics 2021-03-16 Michael Gastegger , Jörg Behler , Philipp Marquetand

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret…

We introduce a machine-learning approach for identifying hidden structural features of open quantum dynamics under restricted experimental access. Unlike most existing data-driven methods which focus on detection or prediction of dynamical…

Quantum Physics · Physics 2026-04-02 Alexander Teretenkov , Sergey Kuznetsov , Alexander Pechen

To go from (passive) process monitoring to active process control, an effective AI system must learn about the behavior of the complex system from very limited training data, forming an ad-hoc digital twin with respect to process inputs and…

Data-driven methods for the identification of the governing equations of dynamical systems or the computation of reduced surrogate models play an increasingly important role in many application areas such as physics, chemistry, biology, and…

Dynamical Systems · Mathematics 2024-12-17 Stefan Klus , Hongyu Zhu

Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale…

We present a universal approach to the investigation of the dynamics in generalized models. In these models the processes that are taken into account are not restricted to specific functional forms. Therefore a single generalized models can…

Chaotic Dynamics · Physics 2007-05-23 Thilo Gross , Ulrike Feudel

Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics…

High Energy Physics - Phenomenology · Physics 2019-01-30 Christoph Englert , Peter Galler , Philip Harris , Michael Spannowsky

Inferring control parameters in non-linear dynamical systems is an important task in analysing general dynamical behaviours, particularly in the presence of inherently deterministic chaos. Traditional approaches often rely on…

Chaotic Dynamics · Physics 2025-06-19 L. Lober , M. S. Palmero , F. A. Rodrigues

Dynamics model learning deals with the task of inferring unknown dynamics from measurement data and predicting the future behavior of the system. A typical approach to address this problem is to train recurrent models. However, predictions…

Machine Learning · Computer Science 2024-01-31 Katharina Ensinger , Sebastian Ziesche , Sebastian Trimpe

World models aim to improve robotic decision making by predicting the consequences of actions. However, in practice, their predictions often become unreliable once the robot encounters states outside the training distribution, limiting…

Robotics · Computer Science 2026-05-18 Tuo An , Jindou Jia , Gen Li , Jingliang Li , Chuhao Zhou , Pengfei Liu , Bofan Lyu , Jiaqi Bai , Xinying Guo , Geng Li , Jianfei Yang

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical,…

Materials Science · Physics 2022-03-22 Sanket Kadulkar , Zachary M. Sherman , Venkat Ganesan , Thomas M. Truskett

Widespread deployment of societal-scale machine learning systems necessitates a thorough understanding of the resulting long-term effects these systems have on their environment, including loss of trustworthiness, bias amplification, and…

Machine Learning · Computer Science 2024-05-07 Andrey Veprikov , Alexander Afanasiev , Anton Khritankov

We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary…

Methodology · Statistics 2025-07-11 Sudipto Banerjee , Xiang Chen , Ian Frankenburg , Daniel Zhou

Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically…

Quantitative Methods · Quantitative Biology 2015-06-11 Yohei Kondo , Kunihiko Kaneko , Shuji Ishihara

By extending the extreme learning machine by additional control inputs, we achieved almost complete reproduction of bifurcation structures of dynamical systems. The learning ability of the proposed neural network system is striking in that…

Chaotic Dynamics · Physics 2024-10-21 Satoru Tadokoro , Akihiro Yamaguchi , Takao Namiki , Ichiro Tsuda

Training modern large language models (LLMs) has become a veritable smorgasbord of algorithms and datasets designed to elicit particular behaviors, making it critical to develop techniques to understand the effects of datasets on the…

Machine Learning · Computer Science 2026-02-05 Ishaq Aden-Ali , Noah Golowich , Allen Liu , Abhishek Shetty , Ankur Moitra , Nika Haghtalab

Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how different architectures may lead to inductive bias when trained…

Machine Learning · Computer Science 2024-03-27 Cédric Gerbelot , Avetik Karagulyan , Stefani Karp , Kavya Ravichandran , Menachem Stern , Nathan Srebro

The study of mechanistic interpretability aims to reverse-engineer a model to explain its behaviors. While recent studies have focused on the static mechanism of a certain behavior, the learning dynamics inside a model remain to be…

Machine Learning · Computer Science 2025-09-24 Yueyan Li , Wenhao Gao , Caixia Yuan , Xiaojie Wang

This article addresses the challenge of adapting data-based models over time. We propose a novel two-fold modelling architecture designed to correct plant-model mismatch caused by two types of uncertainty. Out-of-domain uncertainty arises…

Systems and Control · Electrical Eng. & Systems 2025-07-17 Laura Boca de Giuli , Alessio La Bella , Riccardo Scattolini