{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/conditional-deep-surrogate-models-for","title":"Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems","arxiv_id":"1901.04878","date":"2019-01-15","proceeding":null,"authors":["Yibo Yang","Paris Perdikaris"],"abstract":"We present a probabilistic deep learning methodology that enables the\nconstruction of predictive data-driven surrogates for stochastic systems.\nLeveraging recent advances in variational inference with implicit\ndistributions, we put forth a statistical inference framework that enables the\nend-to-end training of surrogate models on paired input-output observations\nthat may be stochastic in nature, originate from different information sources\nof variable fidelity, or be corrupted by complex noise processes. The resulting\nsurrogates can accommodate high-dimensional inputs and outputs and are able to\nreturn predictions with quantified uncertainty. The effectiveness our approach\nis demonstrated through a series of canonical studies, including the regression\nof noisy data, multi-fidelity modeling of stochastic processes, and uncertainty\npropagation in high-dimensional dynamical systems.","url_abs":"http://arxiv.org/abs/1901.04878v1","url_pdf":"http://arxiv.org/pdf/1901.04878v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"conditional-deep-surrogate-models-for","repo_url":"https://github.com/PredictiveIntelligenceLab/CADGMs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"conditional-deep-surrogate-models-for","repo_url":"https://github.com/ybyangpku/CADGMs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"probabilistic-deep-learning","task_name":"Probabilistic Deep Learning"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}