Beyond Simulate-Then-Optimize: Geothermal AI for Geothermal Dynamics Prediction, Design, and Discovery

The central bottleneck in computational geothermal science is not simulator fidelity or data scarcity—it 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—one 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.