Relevant bibliographies by topics / Neural Network Embeddings

Contents

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

Academic literature on the topic 'Neural Network Embeddings'

Author: Grafiati
Published: 7 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Neural Network Embeddings"

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Che, Feihu, Dawei Zhang, Jianhua Tao, Mingyue Niu, and Bocheng Zhao. "ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2774–81. http://dx.doi.org/10.1609/aaai.v34i03.5665.

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We study the task of learning entity and relation embeddings in knowledge graphs for predicting missing links. Previous translational models on link prediction make use of translational properties but lack enough expressiveness, while the convolution neural network based model (ConvE) takes advantage of the great nonlinearity fitting ability of neural networks but overlooks translational properties. In this paper, we propose a new knowledge graph embedding model called ParamE which can utilize the two advantages together. In ParamE, head entity embeddings, relation embeddings and tail entity e
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Huang, Junjie, Huawei Shen, Liang Hou, and Xueqi Cheng. "SDGNN: Learning Node Representation for Signed Directed Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 196–203. http://dx.doi.org/10.1609/aaai.v35i1.16093.

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Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i.e., status theory and balance theory) and conduct empirical studies o
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Armandpour, Mohammadreza, Patrick Ding, Jianhua Huang, and Xia Hu. "Robust Negative Sampling for Network Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3191–98. http://dx.doi.org/10.1609/aaai.v33i01.33013191.

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Many recent network embedding algorithms use negative sampling (NS) to approximate a variant of the computationally expensive Skip-Gram neural network architecture (SGA) objective. In this paper, we provide theoretical arguments that reveal how NS can fail to properly estimate the SGA objective, and why it is not a suitable candidate for the network embedding problem as a distinct objective. We show NS can learn undesirable embeddings, as the result of the “Popular Neighbor Problem.” We use the theory to develop a new method “R-NS” that alleviates the problems of NS by using a more intelligent
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Srinidhi, K., T. L.S Tejaswi, CH Rama Rupesh Kumar, and I. Sai Siva Charan. "An Advanced Sentiment Embeddings with Applications to Sentiment Based Result Analysis." International Journal of Engineering & Technology 7, no. 2.32 (2018): 393. http://dx.doi.org/10.14419/ijet.v7i2.32.15721.

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We propose an advanced well-trained sentiment analysis based adoptive analysis “word specific embedding’s, dubbed sentiment embedding’s”. Using available word and phrase embedded learning and trained algorithms mainly make use of contexts of terms but ignore the sentiment of texts and analyzing the process of word and text classifications. sentimental analysis on unlike words conveying same meaning matched to corresponding word vector. This problem is bridged by combining encoding opinion carrying text with sentiment embeddings words. But performing sentimental analysis on e-commerce, social n
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Kamath, S., K. G. Karibasappa, Anvitha Reddy, Arati M. Kallur, B. B. Priyanka, and B. P. Bhagya. "Improving the Relation Classification Using Convolutional Neural Network." IOP Conference Series: Materials Science and Engineering 1187, no. 1 (2021): 012004. http://dx.doi.org/10.1088/1757-899x/1187/1/012004.

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Abstract Relation extraction has been the emerging research topic in the field of Natural Language Processing. The proposed work classifies the relations among the data considering the semantic relevance of words using word2vec embeddings towards training the convolutional neural network. We intended to use the semantic relevance of the words in the document to enrich the learning of the embeddings for improved classification. We designed a framework to automatically extract the relations between the entities using deep learning techniques. The framework includes pre-processing, extracting the
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Liu, Ruoyu. "Exploring the Impact of Word2Vec Embeddings Across Neural Network Architectures for Sentiment Analysis." Applied and Computational Engineering 97, no. 1 (2024): 93–98. http://dx.doi.org/10.54254/2755-2721/97/2024melb0085.

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Abstract. Sentiment analysis is crucial for understanding public opinion, gauging customer satisfaction, and making informed business decisions based on the emotional tone of textual data. This study investigates the performance of different Word2Vec-based embedding strategies static, non-static, and multichannel for sentiment analysis across various neural network architectures, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). Despite the rise of advanced contextual embedding methods such as Bidirectional Encoder Representations fr
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Liu, Ruoyu. "Exploring the Impact of Word2Vec Embeddings Across Neural Network Architectures for Sentiment Analysis." Applied and Computational Engineering 94, no. 1 (2024): 106–11. http://dx.doi.org/10.54254/2755-2721/94/2024melb0085.

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Abstract. Sentiment analysis is crucial for understanding public opinion, gauging customer satisfaction, and making informed business decisions based on the emotional tone of textual data. This study investigates the performance of different Word2Vec-based embedding strategies static, non-static, and multichannel for sentiment analysis across various neural network architectures, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). Despite the rise of advanced contextual embedding methods such as Bidirectional Encoder Representations fr
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Gu, Haishuo, Jinguang Sui, and Peng Chen. "Graph Representation Learning for Street-Level Crime Prediction." ISPRS International Journal of Geo-Information 13, no. 7 (2024): 229. http://dx.doi.org/10.3390/ijgi13070229.

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In contemporary research, the street network emerges as a prominent and recurring theme in crime prediction studies. Meanwhile, graph representation learning shows considerable success, which motivates us to apply the methodology to crime prediction research. In this article, a graph representation learning approach is utilized to derive topological structure embeddings within the street network. Subsequently, a heterogeneous information network that incorporates both the street network and urban facilities is constructed, and embeddings through link prediction tasks are obtained. Finally, the
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Zhang, Lei, Feng Qian, Jie Chen, and Shu Zhao. "An Unsupervised Rapid Network Alignment Framework via Network Coarsening." Mathematics 11, no. 3 (2023): 573. http://dx.doi.org/10.3390/math11030573.

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Network alignment aims to identify the correspondence of nodes between two or more networks. It is the cornerstone of many network mining tasks, such as cross-platform recommendation and cross-network data aggregation. Recently, with the development of network representation learning techniques, researchers have proposed many embedding-based network alignment methods. The effect is better than traditional methods. However, several issues and challenges remain for network alignment tasks, such as lack of labeled data, mapping across network embedding spaces, and computational efficiency. Based
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Truică, Ciprian-Octavian, Elena-Simona Apostol, Maria-Luiza Șerban, and Adrian Paschke. "Topic-Based Document-Level Sentiment Analysis Using Contextual Cues." Mathematics 9, no. 21 (2021): 2722. http://dx.doi.org/10.3390/math9212722.

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Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data using pre-trained word embeddings without considering the local context that can be extracted from the dataset, or they detect the overall topic polarity without considering both the local and global context. In this paper, we propose a novel document-topic embedding model, DocTopic2Vec, for document-level polarity detection in large texts by emplo
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Dissertations / Theses on the topic "Neural Network Embeddings"

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Embretsén, Niklas. "Representing Voices Using Convolutional Neural Network Embeddings." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261415.

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In today’s society services centered around voices are gaining popularity. Being able to provide the users with voices they like, to obtain and sustain their attention, is of importance for enhancing the overall experience of the service. Finding an efficient way of representing voices such that similarity comparisons can be performed is therefore of great use. In the field of Natural Language Processing great progress has been made using embeddings from Deep Learning models to represent words in an unsupervised fashion. These representations managed to capture the semantics of the words. This
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Bopaiah, Jeevith. "A recurrent neural network architecture for biomedical event trigger classification." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/73.

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A “biomedical event” is a broad term used to describe the roles and interactions between entities (such as proteins, genes and cells) in a biological system. The task of biomedical event extraction aims at identifying and extracting these events from unstructured texts. An important component in the early stage of the task is biomedical trigger classification which involves identifying and classifying words/phrases that indicate an event. In this thesis, we present our work on biomedical trigger classification developed using the multi-level event extraction dataset. We restrict the scope of o
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PALUMBO, ENRICO. "Knowledge Graph Embeddings for Recommender Systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850588.

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Pettersson, Fredrik. "Optimizing Deep Neural Networks for Classification of Short Texts." Thesis, Luleå tekniska universitet, Datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76811.

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This master's thesis investigates how a state-of-the-art (SOTA) deep neural network (NN) model can be created for a specific natural language processing (NLP) dataset, the effects of using different dimensionality reduction techniques on common pre-trained word embeddings and how well this model generalize on a secondary dataset. The research is motivated by two factors. One is that the construction of a machine learning (ML) text classification (TC) model is typically done around a specific dataset and often requires a lot of manual intervention. It's therefore hard to know exactly what proce
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Revanur, Vandan, and Ayodeji Ayibiowu. "Automatic Generation of Descriptive Features for Predicting Vehicle Faults." Thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-42885.

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Predictive Maintenance (PM) has been increasingly adopted in the Automotive industry, in the recent decades along with conventional approaches such as the Preventive Maintenance and Diagnostic/Corrective Maintenance, since it provides many advantages to estimate the failure before the actual occurrence proactively, and also being adaptive to the present status of the vehicle, in turn allowing flexible maintenance schedules for efficient repair or replacing of faulty components. PM necessitates the storage and analysis of large amounts of sensor data. This requirement can be a challenge in depl
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Murugan, Srikala. "Determining Event Outcomes from Social Media." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703427/.

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An event is something that happens at a time and location. Events include major life events such as graduating college or getting married, and also simple day-to-day activities such as commuting to work or eating lunch. Most work on event extraction detects events and the entities involved in events. For example, cooking events will usually involve a cook, some utensils and appliances, and a final product. In this work, we target the task of determining whether events result in their expected outcomes. Specifically, we target cooking and baking events, and characterize event outcomes into two
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De, Vine Lance. "Analogical frames by constraint satisfaction." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/198036/1/Lance_De%20Vine_Thesis.pdf.

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This research develops a new and efficient constraint satisfaction approach to the unsupervised discovery of linguistic analogies. It shows that systems of analogies can be discovered with high confidence in natural language text by a computer program without human input. The discovery of analogies is useful for many applications such as the construction of linguistic resources, natural language processing and the automation of inference and reasoning.
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Horn, Franziska Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] [Müller, Alan [Gutachter] Akbik, and Ziawasch [Gutachter] Abedjan. "Similarity encoder: A neural network architecture for learning similarity preserving embeddings / Franziska Horn ; Gutachter: Klaus-Robert Müller, Alan Akbik, Ziawasch Abedjan ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1210998386/34.

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Horn, Franziska [Verfasser], Klaus-Robert [Akademischer Betreuer] [Gutachter] Müller, Alan [Gutachter] Akbik, and Ziawasch [Gutachter] Abedjan. "Similarity encoder: A neural network architecture for learning similarity preserving embeddings / Franziska Horn ; Gutachter: Klaus-Robert Müller, Alan Akbik, Ziawasch Abedjan ; Betreuer: Klaus-Robert Müller." Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1210998386/34.

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Šůstek, Martin. "Word2vec modely s přidanou kontextovou informací." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363837.

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This thesis is concerned with the explanation of the word2vec models. Even though word2vec was introduced recently (2013), many researchers have already tried to extend, understand or at least use the model because it provides surprisingly rich semantic information. This information is encoded in N-dim vector representation and can be recall by performing some operations over the algebra. As an addition, I suggest a model modifications in order to obtain different word representation. To achieve that, I use public picture datasets. This thesis also includes parts dedicated to word2vec extensio
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Books on the topic "Neural Network Embeddings"

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Unger, Herwig, and Wolfgang A. Halang, eds. Autonomous Systems 2016. VDI Verlag, 2016. http://dx.doi.org/10.51202/9783186848109.

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To meet the expectations raised by the terms Industrie 4.0, Industrial Internet and Internet of Things, real innovations are necessary, which can be brought about by information processing systems working autonomously. Owing to their growing complexity and their embedding in complex environments, their design becomes increasingly critical. Thus, the topics addressed in this book span from verification and validation of safety-related control software and suitable hardware designed for verifiability to be deployed in embedded systems over approaches to suppress electromagnetic interferences to
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Stöcker, Lars Fredrik, ed. Bridging the Baltic Sea. The Rowman & Littlefield Publishing Group, Inc., 2017. https://doi.org/10.5040/9781666986440.

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Tracing the origins, evolution, and goals of Polish and Estonian émigré politics in Cold War Sweden and its linkages with both the host and homeland societies, this book investigates the transnational dimension of resistance and opposition to the communist regimes in Central and Eastern Europe. The analysis of the constantly shifting, at times conspiratorial, and even subversive networks that transcended the Iron Curtain draws a line from World War II to the collapse of the Soviet Union, framing half a century of transnationally concerted political activism in a geographical context that has n
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Book chapters on the topic "Neural Network Embeddings"

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Zhang, Yuan, Jian Cao, Jue Chen, Wenyu Sun, and Yuan Wang. "Razor SNN: Efficient Spiking Neural Network with Temporal Embeddings." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44192-9_33.

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Markov, Ilia, Helena Gómez-Adorno, Juan-Pablo Posadas-Durán, Grigori Sidorov, and Alexander Gelbukh. "Author Profiling with Doc2vec Neural Network-Based Document Embeddings." In Advances in Soft Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62428-0_9.

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Bajaj, Ahsaas, Shubham Krishna, Hemant Tiwari, and Vanraj Vala. "Learning Mobile App Embeddings Using Multi-task Neural Network." In Natural Language Processing and Information Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23281-8_3.

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Röchert, Daniel, German Neubaum, and Stefan Stieglitz. "Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models." In Disinformation in Open Online Media. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_8.

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Abstract Since social media have increasingly become forums to exchange personal opinions, more and more approaches have been suggested to analyze those sentiments automatically. Neural networks and traditional machine learning methods allow individual adaption by training the data, tailoring the algorithm to the particular topic that is discussed. Still, a great number of methodological combinations involving algorithms (e.g., recurrent neural networks (RNN)), techniques (e.g., word2vec), and methods (e.g., Skip-Gram) are possible. This work offers a systematic comparison of sentiment analyti
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Calderaro, Salvatore, Giosué Lo Bosco, Filippo Vella, and Riccardo Rizzo. "Breast Cancer Histologic Grade Identification by Graph Neural Network Embeddings." In Bioinformatics and Biomedical Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34960-7_20.

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Picone, Rico A. R., Dane Webb, Finbarr Obierefu, and Jotham Lentz. "New Methods for Metastimuli: Architecture, Embeddings, and Neural Network Optimization." In Augmented Cognition. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78114-9_21.

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Biswas, Arijit, Mukul Bhutani, and Subhajit Sanyal. "MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71273-4_13.

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Salsal, Sura Khalid, and Wafaa ALhamed. "Document Retrieval in Text Archives Using Neural Network-Based Embeddings Compared to TFIDF." In Intelligent Systems and Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2094-2_63.

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Molokwu, Bonaventure C., Shaon Bhatta Shuvo, Narayan C. Kar, and Ziad Kobti. "Node Classification in Complex Social Graphs via Knowledge-Graph Embeddings and Convolutional Neural Network." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50433-5_15.

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Barbaglia, Luca, Sergio Consoli, and Sebastiano Manzan. "Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting." In Mining Data for Financial Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_11.

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AbstractForecasting economic and financial variables is a challenging task for several reasons, such as the low signal-to-noise ratio, regime changes, and the effect of volatility among others. A recent trend is to extract information from news as an additional source to forecast economic activity and financial variables. The goal is to evaluate if news can improve forecasts from standard methods that usually are not well-specified and have poor out-of-sample performance. In a currently on-going project, our goal is to combine a richer information set that includes news with a state-of-the-art
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Conference papers on the topic "Neural Network Embeddings"

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Moreno-Palancas, Isabela Fons, Raquel Salcedo D�az, Rub�n Ruiz Femenia, and Jos� A. Caballero. "Handling discrete decisions in bilevel optimization via neural network embeddings." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.175350.

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Bilevel optimization is an active area of research within the operations research community due to its ability to capture the interdependencies between two levels of decisions. This study introduces a metamodeling approach for addressing mixed-integer bilevel optimization problems, exploiting the approximation capabilities of neural networks. The proposed methodology employs neural network embeddings to approximate the optimal follower's response, bypassing the inner optimization problem by parametrizing it with continuous leader�s decisions. The use of Rectified Linear Unit (ReLU) activations
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Zhang, Wei, Brian Barr, and John Paisley. "Gaussian Process Neural Network Embeddings for Collaborative Filtering." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00189.

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Rayvanth, N., Shreya Shree S, VenkataHemant Kumar Reddy Challa, Vishwash Sharma, and Manju Venugopalan. "Exploring Sarcasm Detection: Leveraging Neural Network Models with BERT Embeddings." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10626842.

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Roy, Soumik Guha, Adriz Chanda, Prateek Ganguli, et al. "BMC Engine Sequencing with Graph Neural Network Embeddings of Hardware Circuits." In 2025 38th International Conference on VLSI Design and 2025 24th International Conference on Embedded Systems (VLSID). IEEE, 2025. https://doi.org/10.1109/vlsid64188.2025.00041.

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Et-Tolba, Maryam, Charifa Hanin, and Abdelhamid Belmekki. "DL-Based XSS Attack Detection Approach Using LSTM Neural Network with Word Embeddings." In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2024. http://dx.doi.org/10.1109/wincom62286.2024.10655470.

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Wang, Pengxu. "Electronic Archive Classification Method Based on Convolutional Neural Network with Fast Text Embeddings." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872133.

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Pandimadevi, M., Laith Hussein Jasim, D. Sudha, Guttumukkala Prasanthi, P. Venkatapathi, and Ashok Kumar K. "Graph-optimized Neural Networks with Topological Embeddings for Scalable Cyber Threat Detection in Internet of Things Network." In 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES). IEEE, 2025. https://doi.org/10.1109/iccies63851.2025.11032500.

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Peng, Li, Wang Wang, Cheng Yang, Wenhui Xiao, Xiangzheng Fu, and Yifan Chen. "Dual-Stream Heterogeneous Graph Neural Network Based on Zero-Shot Embeddings for Predicting miRNA-Drug Sensitivity." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822267.

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Luo, Dixin, Haoran Cheng, Qingbin Li, and Hongteng Xu. "Coupled Point Process-based Sequence Modeling for Privacy-preserving Network Alignment." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/678.

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Network alignment aims at finding the correspondence of nodes across different networks, which is significant for many applications, e.g., fraud detection and crime network tracing across platforms. In practice, however, accessing the topological information of different networks is often restricted and even forbidden, considering privacy and security issues. Instead, what we observed might be the event sequences of the networks' nodes in the continuous-time domain. In this study, we develop a coupled neural point process-based (CPP) sequence modeling strategy, which provides a solution to pri
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Dong, Yuxiao, Ziniu Hu, Kuansan Wang, Yizhou Sun, and Jie Tang. "Heterogeneous Network Representation Learning." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/677.

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Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations. The goal of this problem is to automatically project objects, most commonly, vertices, in an input heterogeneous network into a latent embedding space such that both the structural and relational properties of the network can be encoded and preserved. The embeddings (representations) can be then used as the features to machi
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Reports on the topic "Neural Network Embeddings"

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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models ar
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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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