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This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely, Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict the future behavior of dynamic systems over long periods based on training data from relatively short periods. The multi-dimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. Three numerical examples, including one mathematical example and two nonlinear engineering analysis models, are used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning (DL)-based methods for the three particular examples studied. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.<\/jats:p>","DOI":"10.1115\/1.4054039","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T09:35:59Z","timestamp":1646818559000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":53,"title":["Surrogate Modeling of Nonlinear Dynamic Systems: A Comparative Study"],"prefix":"10.1115","volume":"23","author":[{"given":"Ying","family":"Zhao","sequence":"first","affiliation":[{"name":"University of Michigan-Dearborn Department of Industrial and Manufacturing Systems Engineering, , 2340 Heinz Prechter Engineering Complex, Dearborn, MI 48128"}]},{"given":"Chen","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Michigan-Dearborn Department of Industrial and Manufacturing Systems Engineering, , 2340 Heinz Prechter Engineering Complex, Dearborn, MI 48128"}]},{"given":"Manuel A.","family":"Vega","sequence":"additional","affiliation":[{"name":"Advanced Engineering Analysis Group , , Los Alamos, NM 87545"},{"name":"Los Alamos National Laboratory , , Los Alamos, NM 87545"}]},{"given":"Michael D.","family":"Todd","sequence":"additional","affiliation":[{"name":"University of California San Diego Department of Structural Engineering, , La Jolla, CA 92093"}]},{"given":"Zhen","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Michigan-Dearborn Department of Industrial and Manufacturing Systems Engineering, , 2340 Heinz Prechter Engineering Complex, Dearborn, MI 48128"}]}],"member":"33","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"issue":"21","key":"2023051700034019900_CIT0001","doi-asserted-by":"publisher","first-page":"5663","DOI":"10.1016\/j.ces.2005.05.028","article-title":"A Generalized Framework for Solving Dynamic Optimization Problems Using the Artificial Chemical Process Paradigm: Applications to Particulate Processes and Discrete Dynamic Systems","volume":"60","author":"Irizarry","year":"2005","journal-title":"Chem. 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