{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:10:51Z","timestamp":1784099451509,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819227587","type":"print"},{"value":"9789819227594","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-2759-4_29","type":"book-chapter","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:52:45Z","timestamp":1784098365000},"page":"392-403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-Informed Gaussian Process with\u00a0Sequential Virtual Point Selection for\u00a0Constrained Regression"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0366-0606","authenticated-orcid":false,"given":"Wang","family":"Yanlin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Zhijun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Zichen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Zhengqiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Jiying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"29_CR1","unstructured":"Agrell, C.: Gaussian processes with linear operator inequality constraints. J. Mach. Learn. Res. (2019)"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Alvi, S.M.A.A., Janssen, J., Khatamsaz, D., Perez, D., Allaire, D., Arr\u00f3yave, R.: Hierarchical gaussian process-based bayesian optimization for materials discovery in high entropy alloy spaces. Acta Materialia 120908 (2025). https:\/\/doi.org\/10.1016\/j.actamat.2025.120908","DOI":"10.1016\/j.actamat.2025.120908"},{"key":"29_CR3","doi-asserted-by":"publisher","unstructured":"Cross, E.J., Rogers, T.J., Pitchforth, D.J., Gibson, S.J., Zhang, S., Jones, M.R.: A spectrum of physics-informed gaussian processes for regression in engineering. Data-Centric Eng. 5, e8 (2024). https:\/\/doi.org\/10.1017\/dce.2024.2","DOI":"10.1017\/dce.2024.2"},{"key":"29_CR4","unstructured":"Dalton, D., Husmeier, D., Gao, H.: Physics and lie symmetry informed gaussian processes. In: ICML (2024)"},{"key":"29_CR5","unstructured":"Dalton, D., Lazarus, A., Gao, H., Husmeier, D.: Boundary constrained gaussian processes for robust physics-informed machine learning of linear partial differential equations. J. Mach. Learn. Res. 25(1), 272:13096\u2013272:13156 (2025)"},{"key":"29_CR6","doi-asserted-by":"publisher","unstructured":"Gulian, M., Frankel, A., Swiler, L.: Gaussian process regression constrained by boundary value problems. Comput. Meth. Appl. Mech. Eng. 388, 114117 (2022). https:\/\/doi.org\/10.1016\/j.cma.2021.114117","DOI":"10.1016\/j.cma.2021.114117"},{"issue":"6","key":"29_CR7","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","volume":"3","author":"GE Karniadakis","year":"2021","unstructured":"Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L.: Physics-informed machine learning. Nature Rev. Phys. 3(6), 422\u2013440 (2021). https:\/\/doi.org\/10.1038\/s42254-021-00314-5","journal-title":"Nature Rev. Phys."},{"issue":"4","key":"29_CR8","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1198\/TECH.2009.08040","volume":"51","author":"JL Loeppky","year":"2009","unstructured":"Loeppky, J.L., Sacks, J., Welch, W.J.: Choosing the sample size of a computer experiment: a practical guide. Technometrics 51(4), 366\u2013376 (2009). https:\/\/doi.org\/10.1198\/TECH.2009.08040","journal-title":"Technometrics"},{"key":"29_CR9","series-title":"Springer Proceedings in Mathematics & Statistics","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/978-3-030-43465-6_18","volume-title":"Monte Carlo and Quasi-Monte Carlo Methods","author":"AF L\u00f3pez-Lopera","year":"2020","unstructured":"L\u00f3pez-Lopera, A.F., Bachoc, F., Durrande, N., Rohmer, J., Idier, D., Roustant, O.: Approximating gaussian process emulators with linear inequality constraints and noisy observations via MC and MCMC. In: Tuffin, B., L\u2019Ecuyer, P. (eds.) MCQMC 2018. SPMS, vol. 324, pp. 363\u2013381. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43465-6_18"},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"L\u00f3pez-Lopera, A.F., Bachoc, F., Durrande, N., Roustant, O.: Finite-dimensional gaussian approximation with linear inequality constraints. SIAM\/ASA J. Uncertainty Quantif. 6(3), 1224\u20131255 (2018). https:\/\/doi.org\/10.1137\/17M1153157","DOI":"10.1137\/17M1153157"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Lopera, A.F., Bachoc, F., Roustant, O.: High-dimensional additive gaussian processes under monotonicity constraints. In: NIPS (2022)","DOI":"10.52202\/068431-0584"},{"key":"29_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110094","volume":"247","author":"A Marrel","year":"2024","unstructured":"Marrel, A., Iooss, B.: Probabilistic surrogate modeling by gaussian process: a review on recent insights in estimation and validation. Reliab. Eng. Syst. Saf. 247, 110094 (2024). https:\/\/doi.org\/10.1016\/j.ress.2024.110094","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"29_CR13","doi-asserted-by":"publisher","unstructured":"Menz, M., Dubreuil, S., Morio, J., Gogu, C., Bartoli, N., Chiron, M.: Variance based sensitivity analysis for monte Carlo and importance sampling reliability assessment with gaussian processes. Struct. Saf. 93, 102116 (2021). https:\/\/doi.org\/10.1016\/j.strusafe.2021.102116","DOI":"10.1016\/j.strusafe.2021.102116"},{"key":"29_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.107741","volume":"159","author":"D Pitchforth","year":"2021","unstructured":"Pitchforth, D., Rogers, T., Tygesen, U., Cross, E.: Grey-box models for wave loading prediction. Mech. Syst. Signal Process. 159, 107741 (2021). https:\/\/doi.org\/10.1016\/j.ymssp.2021.107741","journal-title":"Mech. Syst. Signal Process."},{"issue":"2","key":"29_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1615\/JMachLearnModelComput.2020035155","volume":"1","author":"L Swiler","year":"2020","unstructured":"Swiler, L., Gulian, M., Frankel, A., Safta, C., Jakeman, J.: A survey of constrained gaussian process regression: approaches and implementation challenges. J. Mach. Learn. Model. Comput. 1(2), 119\u2013156 (2020). https:\/\/doi.org\/10.1615\/JMachLearnModelComput.2020035155","journal-title":"J. Mach. Learn. Model. Comput."},{"key":"29_CR16","doi-asserted-by":"publisher","unstructured":"Veiga, S.D., Marrel, A.: Gaussian process regression with linear inequality constraints. Reliab. Eng. Syst. Saf. 195, 106732 (2020). https:\/\/doi.org\/10.1016\/j.ress.2019.106732","DOI":"10.1016\/j.ress.2019.106732"},{"key":"29_CR17","doi-asserted-by":"publisher","unstructured":"Willard, J., Jia, X., Xu, S., Steinbach, M., Kumar, V.: Integrating scientific knowledge with machine learning for engineering and environmental systems. ACM Comput. Surv. 55(4), 66:1\u201366:37 (2022). https:\/\/doi.org\/10.1145\/3514228","DOI":"10.1145\/3514228"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2759-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:52:46Z","timestamp":1784098366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2759-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"ISBN":["9789819227587","9789819227594"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2759-4_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,16]]},"assertion":[{"value":"16 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ksem2026.rosc.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}