arXiv:2611.09616v1 Announce Type: new Abstract: Coupled thermo-hydro-mechanical (THM) models describe interactions among displacement, fluid pressure, and temperature in geothermal systems. Fine-mesh simulations over many time steps are costly, particularly with heterogeneous permeability and thermal conductivity or fracture networks that alter fluid and heat transport. Proper orthogonal decomposition (POD) reduces the number of unknowns but can require many modes because its reconstruction is linear. We apply SparseModesNet to reconstruct coupled THM solutions using a small set of selected modes and a neural-network nonlinear decoder. Starting from a larger collection of candidate modes, SparseModesNet selects modes to reduce reconstruction error rather than simply retaining those with the highest energy. We compare SparseModesNet with POD on three problems: a two-dimensional heterogeneous medium, a two-dimensional connected fracture network, and a three-dimensional geometry based on Utah FORGE fractures. We evaluate reconstructed displacement, pressure, and temperature fields, as well as pressure- and temperature-based quantities of interest. We also test temporal interpolation by training on a subset of snapshots and reconstructing omitted snapshots within the same time interval. For the same reduced dimension, SparseModesNet achieves smaller reconstruction errors than POD and accurately recovers the solution fields and selected geothermal quantities of interest.