[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119192-en":3,"doc-seo-119192-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119192,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Physics-Enhanced Machine Learning - a position paper for dynamical systems investigations","Physics-Enhanced Machine Learning (PEML) is presented as a position paper focused on strategies for dynamical-system challenges, also referred to as Scientific Machine Learning. The motivation to move beyond standard ML includes limited informative data, avoiding accurate-but-wrong predictions, handling uncertainty, and enabling explainable and interpretable inferences. A general definition is built from four physics and domain-knowledge biases, and three PEML approach groups—physics-guided, physics-encoded, and physics-informed—are discussed. Advantages and challenges for high-consequence engineering decision-making are covered.","Journal of Physics: Conference Series 2909 (2024) 012034 doi:10.1088/1742-6596/2909/1/012034  \nPhysics-Enhanced Machine Learning: a position paper for dynamical systems investigations  \nAlice Cicirello  \nDepartment of Engineering, University of Cambridge, Trumpington Street, CB2 1PZ, Cambridge  \nE-mail: [ac685@cam.ac.uk](ac685@cam.ac.uk)  \nAbstract. This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) - also known as Scientific Machine Learning - with particular focus to those PEML strategies developed to tackle dynamical systems’ challenges. The need to go beyond Machine Learning (ML) strategies is driven by: (i) limited volume of informative data, (ii) avoiding accurate-but-wrong predictions; (iii) dealing with uncertainties; (iv) providing Explainable and Interpretable inferences. A general definition of PEML is provided by considering four physics and domain knowledge biases, and three broad groups of PEML approaches are discussed: physics-guided, physics-encoded and physics-informed. The advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems, are presented.  \n1. Introduction  \nDynamical system models are used in engineering and applied science fields to identify, analyze, control and predict a wide range of behaviours of real-world systems which are typically composed by many components, which may interact with each other, and for which a linear time-invariant modelling assumption is often inadequate [1] . Systems are objects producing a signal (output), depending on other signals (inputs and disturbances) and depending on some initial conditions. Dynamical systems can be used to investigate problems in applied mechanics and structural dynamics that are usually multi-scale, multi-physics, high-dimensional, strongly nonlinear, and time-dependent [1, 2, 3] . Machine Learning (ML) approaches provide a very powerful set of tools for building the nonlinear dynamical systems model directly from observations. However, in many engineering applications, data is typically sparse, noisy, expensive to acquire, and ultimately quite limited. The resulting ML models based on dataonly cannot be fully deployed for guiding high-consequence decision making because they lack (i) generalisation to previously unseen conditions; (ii) interpretability; and (iii) robustness [4] .  \nScientific Machine Learning (SciML) was first mentioned in the report summarising the results of a workshop [4] . In general, SciML refers to the combination of computational science and ML approaches to leverage existing knowledge and physics models within learning schemes [5] . Much of the efforts in SciML have focused on accelerating solvers and on constraining ML-predictions by incorporating physics biases on data-driven architectures [3, 5, 6], consequently, PhysicsInformed Neural Networks (PINNs) architectures [3, 6, 7] are sometimes wrongly perceived asthe only SciML learning scheme. The term Physics-Enhanced Artificial Intelligence (PEAI) was introduced in [9] for “intelligently combining models from the domains of artificial intelligence  \nContent from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.  \nPublished under licence by IOP Publishing Ltd 1  \nJournal of Physics: Conference Series 2909 (2024) 012034 doi:10.1088/1742-6596/2909/1/012034  \nor machine learning with physical and expert models”. The distinction between Physics-Guided Neural Networks (NNs), Physics-Informed NNs and Physics-Encoded NNs was presented in [13] . Informed Machine Learning (IML) was introduced in [10] to describe prior knowledge integration into the learning process, and a taxonomy was developed to enable a structured categorization of different approaches. Since 2019, the te","cbCailpP3sqL04Wg","https://ap.wps.com/l/cbCailpP3sqL04Wg","pdf",2083891,1,12,"English","en",105,"# Introduction\n## Why is there a need to go beyond data-driven for dynamical systems?\n## Background and related hybrid physics-data approaches\n# Physics-Enhanced Machine Learning (PEML) definitions and taxonomy","[{\"question\":\"What problems does Physics-Enhanced Machine Learning aim to address for dynamical systems?\",\"answer\":\"It targets limited informative data, the risk of accurate-but-wrong predictions, uncertainty handling, and the need for explainable and interpretable inferences.\"},{\"question\":\"How is PEML defined in the paper?\",\"answer\":\"PEML is defined through four physics and domain-knowledge biases, which supports a framework distinguishing multiple categories of hybrid approaches.\"},{\"question\":\"What are the three broad groups of PEML approaches discussed?\",\"answer\":\"The paper groups approaches into physics-guided, physics-encoded, and physics-informed strategies.\"}]","Physics-Enhanced Machine Learning - 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