{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:59:15Z","timestamp":1781715555941,"version":"3.54.5"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2026,6,17]]},"abstract":"<jats:p>The central bottleneck in computational geothermal science is not simulator fidelity or data scarcity\u2014it is the abstraction itself. Geothermal energy is increasingly important to the clean energy transition, yet its computational core still follows a legacy simulate-then-optimize paradigm: a deterministic simulator is calibrated to sparse observations and then used to optimize decisions within a fixed model. Hidden inside this pipeline are three commitments\u2014one predicted future, one mostly static operating strategy, and one fitted model per site. We argue that, for next-generation enhanced geothermal systems, the subsurface is partially observed, heterogeneous, and intervention-sensitive, and the information available to characterize it is limited. As a result, forecasting and decision-making must reason over multiple physically plausible futures under uncertainty. Our central claim is that geothermal should be reframed as an adaptive problem of inference, intervention, and discovery. Under this view, simulation becomes conditional generation over plausible reservoir futures rather than point prediction of one trajectory. Operation becomes adaptive decision making over belief states rather than offline scheduling under a presumed known state. Calibration becomes the separation of transferable physical structure from site-specific corrections rather than repeated fitting within a fixed equation class. These are not three independent engineering problems; they are three phases of a single inference cycle. This reframing matters because, in geothermal, uncertainty is not merely something to quantify; it is something operations act upon and reshape. Likewise, persistent model mismatch is not merely an engineering nuisance to suppress; it is the primary scientific signal from which missing or site-modulated physics can be discovered. We therefore organize the paper around three consequences of this reframing: generative world models of reservoir evolution, belief-state policy learning for sustainable operation, and data-to-equation discovery for transferable geophysics. Taken together, these directions define a new agenda for geothermal AI beyond faster surrogate prediction toward adaptive subsurface intelligence where inference, intervention, and discovery are intrinsically coupled.<\/jats:p>","DOI":"10.1145\/3820356.3820363","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T15:58:43Z","timestamp":1781711923000},"page":"102-114","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Simulate-Then-Optimize: Geothermal AI for Geothermal Dynamics Prediction, Design, and Discovery"],"prefix":"10.1145","volume":"28","author":[{"given":"Kunpeng","family":"Liu","sequence":"first","affiliation":[{"name":"Clemson University, Clemson, SC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nori","family":"Nakata","sequence":"additional","affiliation":[{"name":"Lawrence Berkeley National Laboratory, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Clemson University, Clemson, SC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Chen","sequence":"additional","affiliation":[{"name":"Lawrence Berkeley National Laboratory, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Kansas, Lawrence, KS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhe","sequence":"additional","affiliation":[{"name":"University of Kansas, Lawrence, KS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Kansas, Lawrence, KS, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyu","family":"Cao","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjie","family":"Fu","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v40i17.38462"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/WSC68292.2025.11338940"},{"key":"e_1_2_1_3_1","first-page":"3360","volume-title":"International Conference on Learning Representations","volume":"2025","author":"Bastek J.-H.","year":"2025","unstructured":"J.-H. 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Implementation of the world\u2019s first greater than 300 c propped egs reservoir."},{"key":"e_1_2_1_22_1","volume-title":"Gaia: Geothermal analytics and intelligent agent. arXiv preprint arXiv:2511.03852","author":"Harsuko R.","year":"2025","unstructured":"R. Harsuko, Z. Bi, and N. Nakata. Gaia: Geothermal analytics and intelligent agent. arXiv preprint arXiv:2511.03852, 2025."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3496298"},{"key":"e_1_2_1_24_1","volume-title":"Enhanced geothermal systems for clean firm energy generation. Nature reviews clean technology, 1(2):148-160","author":"Horne R.","year":"2025","unstructured":"R. Horne, A. Genter, M. McClure, W. Ellsworth, J. Norbeck, and E. Schill. Enhanced geothermal systems for clean firm energy generation. 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The Future of Geothermal Energy: Impact of Enhanced Geothermal Systems (EGS) on the United States in the 21st Century : an Assessment by an MIT-led Interdisciplinary Panel. Massachusetts Institute of Technology 2006."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2024.114875"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.11.031"},{"key":"e_1_2_1_43_1","volume-title":"Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators","author":"Pathak J.","year":"2022","unstructured":"J. Pathak, S. Subramanian, P. Harrington, S. Raja, A. Chattopadhyay, M. Mardani, T. Kurth, D. Hall, Z. Li, K. Azizzadenesheli, P. Hassanzadeh, K. Kashinath, and A. Anandkumar. Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators, 2022."},{"key":"e_1_2_1_44_1","volume-title":"TOUGH2: A general-purpose numerical simulator for multiphase fluid and heat flow. 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Data-efficient symbolic regression via foundation model distillation. arXiv preprint arXiv:2508.19487, 2025."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2026.findings-eacl.191"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.748"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.201"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i25.34876"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICKG63256.2024.00067"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM65498.2025.00102"},{"key":"e_1_2_1_74_1","volume-title":"Leka: Llm-enhanced knowledge augmentation. arXiv preprint arXiv:2501.17802","author":"Zhang X.","year":"2025","unstructured":"X. Zhang, J. Zhang, F. Mo, D. Wang, Y. Fu, and K. Liu. 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