This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …

The energy industry worldwide is today confronted with several challenges, including
heightened levels of consumption and inefficiency, volatile patterns in demand and supply …

Reliable characterization of subsurface structures is essential for earth sciences and related
applications. Data assimilation‐based identification frameworks can reasonably estimate …

The advent of reliable and inexpensive sensors and advancements in general computing
have made data-heavy algorithms feasible for operational, real-time decision-making …

Accurate prediction of the average thermal extraction load (ATEL) in hydrothermal heating
systems optimizes energy recovery, though numerical models are constrained by modeling …

In this work, as an extension of previous machine learning studies, three novel techniques,
namely local interpretable model-agnostic explanations (LIME), neural network pattern …

The integration of technologies has made it possible to develop optimal operating conditions
at reduced costs, which results in a more sustainable energy transition away from fossil fuels …

This research focuses on optimizing geothermal reservoir modeling tackling issues related
to non-uniqueness, subsurface uncertainties, and computational intensity. The proposed …

Geothermal energy as a sustainable and clean energy source depends on the accurate
estimation of reservoir temperatures. Understanding aquifer temperatures is crucial for …

Geothermal reservoirs are highly anisotropic and heterogeneous, and thus require a variety
of structural geology, geomechanical, remote sensing, geophysical and hydraulic …