Relevant bibliographies by topics / Machine Learning Informé

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Machine Learning Informé'

Author: Grafiati
Published: 21 December 2024
Last updated: 31 July 2025

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Journal articles on the topic "Machine Learning Informé"

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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space.
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Pateras, Joseph, Pratip Rana, and Preetam Ghosh. "A Taxonomic Survey of Physics-Informed Machine Learning." Applied Sciences 13, no. 12 (2023): 6892. http://dx.doi.org/10.3390/app13126892.

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Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant prior information. This work discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINNs) in particular are highlighted. Additionally, we explore recent works toward utilizing neural operator learning to intuit relationships in physics systems
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Minasny, Budiman, Toshiyuki Bandai, Teamrat A. Ghezzehei, et al. "Soil Science-Informed Machine Learning." Geoderma 452 (December 2024): 117094. http://dx.doi.org/10.1016/j.geoderma.2024.117094.

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Xypakis, Emmanouil, Valeria deTurris, Fabrizio Gala, Giancarlo Ruocco, and Marco Leonetti. "Physics-informed machine learning for microscopy." EPJ Web of Conferences 266 (2022): 04007. http://dx.doi.org/10.1051/epjconf/202226604007.

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We developed a physics-informed deep neural network architecture able to achieve signal to noise ratio improvements starting from low exposure noisy data. Our model is based on the nature of the photon detection process characterized by a Poisson probability distribution which we included in the training loss function. Our approach surpasses previous algorithms performance for microscopy data, moreover, the generality of the physical concepts employed here, makes it readily exportable to any imaging context.
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Zhao, Hefei, Yinglun Zhan, Joshua Nduwamungu, Yuzhen Zhou, Changmou Xu, and Zheng Xu. "Machine learning-driven Raman spectroscopy for rapidly detecting type, adulteration, and oxidation of edible oils." INFORM International News on Fats, Oils, and Related Materials 31, no. 4 (2020): 12–15. http://dx.doi.org/10.21748/inform.04.2020.12.

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Serre, Thomas. "Deep Learning: The Good, the Bad, and the Ugly." Annual Review of Vision Science 5, no. 1 (2019): 399–426. http://dx.doi.org/10.1146/annurev-vision-091718-014951.

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Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern mac
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Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. "Historical maps inform landform cognition in machine learning." Abstracts of the ICA 6 (August 11, 2023): 1–2. http://dx.doi.org/10.5194/ica-abs-6-10-2023.

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Karimpouli, Sadegh, and Pejman Tahmasebi. "Physics informed machine learning: Seismic wave equation." Geoscience Frontiers 11, no. 6 (2020): 1993–2001. http://dx.doi.org/10.1016/j.gsf.2020.07.007.

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Oneto, Luca, Sandro Ridella, and Davide Anguita. "Informed Machine Learning: Excess risk and generalization." Neurocomputing 646 (September 2025): 130521. https://doi.org/10.1016/j.neucom.2025.130521.

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Zhang, Xi. "Application of Machine Learning in Stock Price Analysis." Highlights in Science, Engineering and Technology 107 (August 15, 2024): 143–49. http://dx.doi.org/10.54097/tjhsx998.

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With the advancement of technology, machine learning has emerged as a powerful tool for analyzing complex financial data, including stock prices. By leveraging algorithms capable of identifying patterns and trends, it offers insights into market behavior. This study explores the application of machine learning techniques in stock price analysis, aiming to enhance prediction accuracy and inform investment decisions. Through rigorous analysis, our research demonstrates that machine learning models can effectively capture the dynamic nature of stock markets, leading to improved forecasting capabi
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Dissertations / Theses on the topic "Machine Learning Informé"

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Guimbaud, Jean-Baptiste. "Enhancing Environmental Risk Scores with Informed Machine Learning and Explainable AI." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10188.

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Dès la conception, des facteurs environnementaux tels que la qualité de l'air ou les habitudes alimentaires peuvent significativement influencer le risque de développer diverses maladies chroniques. Dans la littérature épidémiologique, des indicateurs connus sous le nom de Scores de Risque Environnemental (Environmental Risk Score, ERS) sont utilisés non seulement pour identifier les individus à risque, mais aussi pour étudier les relations entre les facteurs environnementaux et la santé. Une limite de la plupart des ERSs est qu'ils sont exprimés sous forme de combinaisons linéaires d'un nombr
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Doumèche, Nathan. "Physics-informed machine learning : a mathematical framework with applications to time series forecasting." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS105.

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L'apprentissage automatique informé par la physique est un domaine récent qui consiste à intégrer des connaissances physiques dans des modèles d'apprentissage automatique. L'information physique prend souvent la forme d'un système d'équations aux dérivées partielles (EDPs) que la fonction de régression doit satisfaire. Dans la première partie de cette thèse, nous analysons les propriétés statistiques des méthodes d'apprentissage automatique informé par la physique. En particulier, nous étudions les propriétés des réseaux de neurones informés par la physique, en termes d'approximation, de consi
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Quattromini, Michele. "Graph Neural Networks for fluid mechanics : data-assimilation and optimization." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST161.

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Cette thèse de doctorat explore l'application des réseaux de neurones en graphes (GNN) dans le domaine de la dynamique des fluides numérique (CFD), avec un accent particulier sur l'assimilation de données et l'optimisation. Le travail est structuré en trois parties principales: assimilation de données pour les équations de Navier-Stokes moyennées à la Reynolds (RANS) basée sur des modèles GNN; assimilation de données augmentée par les GNN avec des contraintes physiques imposées par la méthode adjointe; optimisation des systèmes fluides par des techniques d'apprentissage automatique (ML).Dans l
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Mack, Jonas. "Physics Informed Machine Learning of Nonlinear Partial Differential Equations." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441275.

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Leung, Jason W. "Application of machine learning : automated trading informed by event driven data." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105982.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 61-65).<br>Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical
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Wu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.

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Reynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, a
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Elabid, Zakaria. "Informed deep learning for modeling physical dynamics." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS006.

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La modélisation des systèmes statiques et dynamiques a été révolutionnée par l'apprentissage profond, qui a offert des outils puissants pour la prévision et l'adaptation à des phénomènes physiques complexes. Cependant, les réseaux de neurones traditionnels peinent souvent à se généraliser, en particulier dans des scénarios hors distribution et avec des ensembles de données réduits, ce qui limite leur application dans des contextes réels. Cette thèse aborde ces défis en proposant des méthodes novatrices qui intègrent directement les lois physiques dans les cadres d'apprentissage profond. La pre
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Reichert, Nils. "CORRELATION BETWEEN COMPUTER RECOGNIZED FACIAL EMOTIONS AND INFORMED EMOTIONS DURING A CASINO COMPUTER GAME." Thesis, Fredericton: University of New Brunswick, 2012. http://hdl.handle.net/1882/44596.

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Emotions play an important role for everyday communication. Different methods allow computers to recognize emotions. Most are trained with acted emotions and it is unknown if such a model would work for recognizing naturally appearing emotions. An experiment was setup to estimate the recognition accuracy of the emotion recognition software SHORE, which could detect the emotions angry, happy, sad, and surprised. Subjects played a casino game while being recorded. The software recognition was correlated with the recognition of ten human observers. The results showed a strong recognition for happ
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Wang, Jianxun. "Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77035.

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Computational fluid dynamics (CFD) has been widely used to simulate turbulent flows. Although an increased availability of computational resources has enabled high-fidelity simulations (e.g. large eddy simulation and direct numerical simulation) of turbulent flows, the Reynolds-Averaged Navier-Stokes (RANS) equations based models are still the dominant tools for industrial applications. However, the predictive capability of RANS models is limited by potential inaccuracies driven by hypotheses in the Reynolds stress closure. With the ever-increasing use of RANS simulations in mission-critical a
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Cedergren, Linnéa. "Physics-informed Neural Networks for Biopharma Applications." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185423.

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Physics-Informed Neural Networks (PINNs) are hybrid models that incorporate differential equations into the training of neural networks, with the aim of bringing the best of both worlds. This project used a mathematical model describing a Continuous Stirred-Tank Reactor (CSTR), to test two possible applications of PINNs. The first type of PINN was trained to predict an unknown reaction rate law, based only on the differential equation and a time series of the reactor state. The resulting model was used inside a multi-step solver to simulate the system state over time. The results showed that t
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Books on the topic "Machine Learning Informé"

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Schulz, Daniel, and Christian Bauckhage, eds. Informed Machine Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83097-6.

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Huang, Qiang. Domain-informed Machine Learning for Smart Manufacturing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91631-1.

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Interpretable Machine Learning for the Analysis, Design, Assessment, and Informed Decision Making for Civil Infrastructure. Elsevier Science & Technology, 2023.

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Madhu, G., Sandeep Kautish, A. Govardhan, and Avinash Sharma, eds. Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post-COVID-19 Landscape. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150792721220101.

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This book gives an overview of innovative approaches in telehealth and telemedicine. The Goal of the content is to inform readers about recent computer applications in e-health, including Internet of Things (IoT) and Internet of Medical Things (IoMT) technology. The 9 chapters will guide readers to determine the urgency to intervene in specific medical cases, and to assess risk to healthcare workers. The focus on telehealth along with telemedicine, encompasses a broader spectrum of remote healthcare services for the reader to understand. Chapters cover the following topics: - A COVID-19 care s
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Smith, Gary, and Jay Cordes. The 9 Pitfalls of Data Science. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844396.001.0001.

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Scientific rigor and critical thinking skills are indispensable in this age of big data because machine learning and artificial intelligence are often led astray by meaningless patterns. The 9 Pitfalls of Data Science is loaded with entertaining real-world examples of both successful and misguided approaches to interpreting data, both grand successes and epic failures. Anyone can learn to distinguish between good data science and nonsense. We are confident that readers will learn how to avoid being duped by data, and make better, more informed decisions. Whether they want to be effective creat
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Anderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.

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This book, “Forest Paths” for short, started as a detailed guide for the construction of predictive models for credit and other risk assessment, for use in big-bank retail lending. It became a textbook covering credit processes (from marketing through to fraud), bureau and rating agencies, and various tools. Included are detailed histories (economics, statistics, social science}, which much referencing. It is unique in the field, with chatpers’-end questions. The primary target market is corporate and academic, but much would be of interest to a broader audience. There are eight modules: 1) an
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El-Nasr, Magy Seif, Alessandro Canossa, Truong-Huy D. Nguyen, and Anders Drachen. Game Data Science. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192897879.001.0001.

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This book is aimed at giving readers an introduction to the practical side of game data science and thus can be used a textbook for game analytics or game user research class or as a reference to self learners and enthusiasts. Game data science is a term that we use to denote a process composed of methods and techniques by which an analyst or a data scientist can make sense of data to allow decision makers in a game company to make informed decisions. This process involves: statistical analysis, visualization, abstraction of low-level data, machine learning and sequence data modeling. The book
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Dowd, Cate. Digital Journalism, Drones, and Automation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190655860.001.0001.

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Advances in online technology and news systems, such as automated reasoning across digital resources and connectivity to cloud servers for storage and software, have changed digital journalism production and publishing methods. Integrated media systems used by editors are also conduits to search systems and social media, but the lure of big data and rise in fake news have fragmented some layers of journalism, alongside investments in analytics and a shift in the loci for verification. Data has generated new roles to exploit data insights and machine learning methods, but access to big data and
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Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that a
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Giudici, Paolo, and Giulio Mignola. Big Data & Advanced Analytics per il Risk Management. AIFIRM, 2022. http://dx.doi.org/10.47473/2016ppa00035.

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One of the main consequences of the digital revolution, which for the last few years has been transforming almost every economic activity, has been an unprecedented availability of big data. At the same time, recent technological breakthroughs have provided tools (technological infrastructures and analytical methodologies) capable of processing these large amounts of data in a very short timeframe. Against this backdrop, the introduction of machine-learning models has been spreading. Even the Banking and Insurance sectors, despite their long-standing tradition of using statistical models, have
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Book chapters on the topic "Machine Learning Informé"

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Bauckhage, Christian, and Rafet Sifa. "Training Support Vector Machines by Solving Differential Equations." In Cognitive Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83097-6_12.

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Abstract The increasingly popular idea of Physics Informed Machine Learning uses trained machine learning models as tools for differential equation solving. Here, we turn this idea upside down and consider differential equation solving as a tool for training machine learning models. We focus on support vector machines for binary classification and explore the merits of training them by means of solving gradient flows. We thus assume a continuous time perspective on a fundamental machine learning problem which, in the mid- to long term, may inform implementations on (re)emerging hardware platfo
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Neuer, Marcus J. "Physics-Informed Learning." In Machine Learning for Engineers. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-69995-9_6.

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Braga-Neto, Ulisses. "Physics-Informed Machine Learning." In Fundamentals of Pattern Recognition and Machine Learning. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60950-3_12.

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Wang, Sifan, and Paris Perdikaris. "Adaptive Training Strategies for Physics-Informed Neural Networks." In Knowledge-Guided Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-6.

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Simm, Jaak, Adam Arany, Edward De Brouwer, and Yves Moreau. "Expressive Graph Informer Networks." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95470-3_15.

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Tsironis, Giorgos. "Epidemiology with Physics Informed Machine Learning." In Understanding Complex Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81946-9_18.

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Afroze, Lameya, Silke Merkelbach, Sebastian von Enzberg, and Roman Dumitrescu. "Domain Knowledge Injection Guidance for Predictive Maintenance." In Machine Learning for Cyber-Physical Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47062-2_8.

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AbstractWith the integration of Industry 4.0 technologies, overall maintenance costs of industrial machines can be reduced by applying predictive maintenance. Unique challenges that often occur in real-time manufacturing environments require the use of domain knowledge from different experts. However, there is hardly any guidance that suggests data scientists how to inject knowledge from predictive maintenance use cases in machine learning models. This paper addresses this lack and presents a guidance for the injection of domain knowledge in machine learning models for predictive maintenance b
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Sun, Alexander Y., Hongkyu Yoon, Chung-Yan Shih, and Zhi Zhong. "Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey." In Knowledge-Guided Machine Learning. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003143376-5.

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Huang, Qiang. "Applications of Process-Informed Optimal Compensation." In Domain-informed Machine Learning for Smart Manufacturing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91631-1_7.

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Dani, Harsh, Jundong Li, and Huan Liu. "Sentiment Informed Cyberbullying Detection in Social Media." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71249-9_4.

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Conference papers on the topic "Machine Learning Informé"

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Oneto, Luca, Nicolò Navarin, Alessio Micheli, et al. "Informed Machine Learning for Complex Data." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-1.

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Oneto, Luca, Davide Anguita, and Sandro Ridella. "Informed Machine Learning: Excess Risk and Generalization." In ESANN 2024. Ciaco - i6doc.com, 2024. http://dx.doi.org/10.14428/esann/2024.es2024-20.

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Osorio Quero, Carlos Alexander, and Jose Martinez-Carranza. "Physics-Informed Machine Learning for UAV Control." In 2024 21st International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2024. https://doi.org/10.1109/cce62852.2024.10770871.

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Farlessyost, William, and Shweta Singh. "Improving Mechanistic Model Accuracy with Machine Learning Informed Physics." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.121371.

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Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data.
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Yu, Yue, Jiageng Tong, Jinhui Xia, Jinya Su, and Shihua Li. "PMSM System Identification by Knowledge-informed Machine Learning." In 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN). IEEE, 2024. https://doi.org/10.1109/indin58382.2024.10774223.

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Zhu, Shijie, Hao Li, Yejie Jiang, and Yingjun Deng. "Inner Defect Detection via Physics-Informed Machine Learning." In 2024 6th International Conference on System Reliability and Safety Engineering (SRSE). IEEE, 2024. https://doi.org/10.1109/srse63568.2024.10772527.

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Zhang, Tianren, Yuanbin Wang, Ruizhe Dong, Wenhu Wang, Zhongxue Yang, and Mingzhu Zhu. "Informed Machine Learning for Real-time Grinding Force Prediction." In 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 2024. http://dx.doi.org/10.1109/m2vip62491.2024.10746047.

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Liu, Zheng, Yuan Jiang, Yumeng Li, and Pingfeng Wang. "Physics-Informed Machine Learning for Battery Pack Thermal Management." In 2025 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2025. https://doi.org/10.1109/rams48127.2025.10935157.

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Sampath, Akila, Omar Faruque, Azim Khan, Vandana Janeja, and Jianwu Wang. "Physics-Informed Machine Learning for Sea Ice Thickness Prediction." In 2024 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2024. https://doi.org/10.1109/ickg63256.2024.00048.

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Park, Taegyun, Eunkoo Lee, Sol Han, et al. "Chemistry-informed machine learning model for EUV photoresist development." In Advances in Patterning Materials and Processes XLII, edited by Douglas Guerrero and Ryan Callahan. SPIE, 2025. https://doi.org/10.1117/12.3049907.

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Reports on the topic "Machine Learning Informé"

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Martinez, Carianne, Jessica Jones, Drew Levin, Nathaniel Trask, and Patrick Finley. Physics-Informed Machine Learning for Epidemiological Models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1706217.

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McDermott, Jason, Song Feng, Christine Chang, Darren Schmidt, and Vincent Danna. Structural- and Functional-Informed Machine Learning for Protein Function Prediction. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1988630.

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Guthrie, George Drake Jr, and Hari S. Viswanathan. Science-informed Machine Learning to Increase Recovery Efficiency in Unconventional Reservoirs. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1614818.

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Wang, Jianxun, Jinlong Wu, Julia Ling, Gianluca Iaccarino, and Heng Xiao. Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1562229.

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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Mueller, Juliane. Machine Learning to Enable Efficient Uncertainty Quantification, Data Assimilation, and Informed Data Acquisition. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769743.

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Athon, Matthew, Danielle Ciesielski, Jordan Corbey, et al. Visualizing Uranium Crystallization from Melt: Experiment-Informed Phase Field Modeling and Machine Learning. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2338176.

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Heo, YeongAe, Joshua Humberston, and Jose Barreras Gonzalez. Evolving Multi-hazard Machine Learning Modeling for Advanced Risk-Informed Infrastructure Resilience Assessment. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2483390.

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Ullrich, Paul, Tapio Schneider, and Da Yang. Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769762.

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