{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:17:54Z","timestamp":1773789474123,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["2026710"],"award-info":[{"award-number":["2026710"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467449","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:21:39Z","timestamp":1628878899000},"page":"237-247","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics"],"prefix":"10.1145","author":[{"given":"Arka","family":"Daw","sequence":"first","affiliation":[{"name":"Virginia Tech, Blacksburg, VA, USA"}]},{"given":"M.","family":"Maruf","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, VA, USA"}]},{"given":"Anuj","family":"Karpatne","sequence":"additional","affiliation":[{"name":"Virginia Tech, Blacksburg, VA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Blending diverse physical priors with neural networks. arXiv preprint arXiv:1910.00201","author":"Ba Yunhao","year":"2019","unstructured":"Yunhao Ba , Guangyuan Zhao , and Achuta Kadambi . 2019. Blending diverse physical priors with neural networks. arXiv preprint arXiv:1910.00201 ( 2019 ). Yunhao Ba, Guangyuan Zhao, and Achuta Kadambi. 2019. Blending diverse physical priors with neural networks. arXiv preprint arXiv:1910.00201 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Francc ois Lanusse, and Urovs Seljak","author":"B\u00f6hm Vanessa","year":"2019","unstructured":"Vanessa B\u00f6hm , Francc ois Lanusse, and Urovs Seljak . 2019 . Uncertainty Quantification with Generative Models . arXiv preprint arXiv:1910.10046 (2019). Vanessa B\u00f6hm, Francc ois Lanusse, and Urovs Seljak. 2019. Uncertainty Quantification with Generative Models. arXiv preprint arXiv:1910.10046 (2019)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.60"},{"key":"e_1_3_2_2_4_1","volume-title":"international conference on machine learning. 1050--1059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . 2016 . Dropout as a bayesian approximation: Representing model uncertainty in deep learning . In international conference on machine learning. 1050--1059 . Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. 1050--1059."},{"key":"e_1_3_2_2_5_1","volume-title":"Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani , Faruk Ahmed , Martin Arjovsky , Vincent Dumoulin , and Aaron Courville . 2017. Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028 ( 2017 ). Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. 2017. Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028 (2017)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-019-01421-8"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.63"},{"key":"e_1_3_2_2_8_1","volume-title":"Hands-on Bayesian Neural Networks--a Tutorial for Deep Learning Users. arXiv preprint arXiv:2007.06823","author":"Jospin Laurent Valentin","year":"2020","unstructured":"Laurent Valentin Jospin , Wray Buntine , Farid Boussaid , Hamid Laga , and Mohammed Bennamoun . 2020. Hands-on Bayesian Neural Networks--a Tutorial for Deep Learning Users. arXiv preprint arXiv:2007.06823 ( 2020 ). Laurent Valentin Jospin, Wray Buntine, Farid Boussaid, Hamid Laga, and Mohammed Bennamoun. 2020. Hands-on Bayesian Neural Networks--a Tutorial for Deep Learning Users. arXiv preprint arXiv:2007.06823 (2020)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2720168"},{"key":"e_1_3_2_2_10_1","volume-title":"Physics-guided neural networks (pgnn): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431","author":"Karpatne Anuj","year":"2017","unstructured":"Anuj Karpatne , William Watkins , Jordan Read , and Vipin Kumar . 2017b. Physics-guided neural networks (pgnn): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431 ( 2017 ). Anuj Karpatne, William Watkins, Jordan Read, and Vipin Kumar. 2017b. Physics-guided neural networks (pgnn): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431 (2017)."},{"key":"e_1_3_2_2_11_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_12_1","unstructured":"Chunyuan Li Jianqiao Li Guoyin Wang and Lawrence Carin. 2018. Learning to sample with adversarially learned likelihood-ratio. (2018).  Chunyuan Li Jianqiao Li Guoyin Wang and Lawrence Carin. 2018. Learning to sample with adversarially learned likelihood-ratio. (2018)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/env.2553"},{"key":"e_1_3_2_2_14_1","volume-title":"International conference on artificial intelligence and statistics. PMLR, 234--244","author":"Pearce Tim","year":"2020","unstructured":"Tim Pearce , Felix Leibfried , and Alexandra Brintrup . 2020 . Uncertainty in neural networks: Approximately bayesian ensembling . In International conference on artificial intelligence and statistics. PMLR, 234--244 . Tim Pearce, Felix Leibfried, and Alexandra Brintrup. 2020. Uncertainty in neural networks: Approximately bayesian ensembling. In International conference on artificial intelligence and statistics. PMLR, 234--244."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_2_2_16_1","volume-title":"Improved techniques for training gans. arXiv preprint arXiv:1606.03498","author":"Salimans Tim","year":"2016","unstructured":"Tim Salimans , Ian Goodfellow , Wojciech Zaremba , Vicki Cheung , Alec Radford , and Xi Chen . 2016. Improved techniques for training gans. arXiv preprint arXiv:1606.03498 ( 2016 ). Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016. Improved techniques for training gans. arXiv preprint arXiv:1606.03498 (2016)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Russell Stewart and Stefano Ermon. 2017. Label-free supervision of neural networks with physics and domain knowledge. In AAAI.  Russell Stewart and Stefano Ermon. 2017. Label-free supervision of neural networks with physics and domain knowledge. In AAAI.","DOI":"10.1609\/aaai.v31i1.10934"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2606428"},{"key":"e_1_3_2_2_19_1","volume-title":"International Conference on Machine Learning. PMLR, 5190--5199","author":"Wang Kuan-Chieh","year":"2018","unstructured":"Kuan-Chieh Wang , Paul Vicol , James Lucas , Li Gu , Roger Grosse , and Richard Zemel . 2018 . Adversarial distillation of bayesian neural network posteriors . In International Conference on Machine Learning. PMLR, 5190--5199 . Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, and Richard Zemel. 2018. Adversarial distillation of bayesian neural network posteriors. In International Conference on Machine Learning. PMLR, 5190--5199."},{"key":"e_1_3_2_2_20_1","volume-title":"Understanding and mitigating gradient pathologies in physics-informed neural networks. arXiv preprint arXiv:2001.04536","author":"Wang Sifan","year":"2020","unstructured":"Sifan Wang , Yujun Teng , and Paris Perdikaris . 2020. Understanding and mitigating gradient pathologies in physics-informed neural networks. arXiv preprint arXiv:2001.04536 ( 2020 ). Sifan Wang, Yujun Teng, and Paris Perdikaris. 2020. Understanding and mitigating gradient pathologies in physics-informed neural networks. arXiv preprint arXiv:2001.04536 (2020)."},{"key":"e_1_3_2_2_21_1","volume-title":"Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919","author":"Willard Jared","year":"2020","unstructured":"Jared Willard , Xiaowei Jia , Shaoming Xu , Michael Steinbach , and Vipin Kumar . 2020. Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919 ( 2020 ). Jared Willard, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2020. Integrating physics-based modeling with machine learning: A survey. arXiv preprint arXiv:2003.04919 (2020)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.05.027"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.07.048"},{"key":"e_1_3_2_2_24_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 1137--1146","author":"Zhang Ruiyi","year":"2018","unstructured":"Ruiyi Zhang , Chunyuan Li , Changyou Chen , and Lawrence Carin . 2018 . Learning structural weight uncertainty for sequential decision-making . In International Conference on Artificial Intelligence and Statistics. PMLR, 1137--1146 . Ruiyi Zhang, Chunyuan Li, Changyou Chen, and Lawrence Carin. 2018. Learning structural weight uncertainty for sequential decision-making. In International Conference on Artificial Intelligence and Statistics. PMLR, 1137--1146."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467449","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467449","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467449","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:37Z","timestamp":1750191517000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":24,"alternative-id":["10.1145\/3447548.3467449","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467449","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}