This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: The key to finding more critical minerals may lie in understanding how fluids and chemicals move through rock. As these fluids travel underground, they can dissolve, transport and concentrate valuable minerals in specific places.
Until now, studying these complex processes has required either time-consuming computer simulations or AI systems that need large amounts of training data. Even then, they sometimes produce unrealistic results. Supported through an Environmental Molecular Sciences Laboratory (EMSL) user project, a team of researchers from the University of Houston and EMSL developed a new AI-powered tool that can predict how fluids and dissolved chemicals move through rocks and other porous materials underground.
The new approach combines AI with the established laws of physics and chemistry to deliver faster, more reliable predictions. "We developed a physics-informed machine learning framework that can rapidly forecast important parameters that cannot be measured using traditional experiments," said Kalyana Nakshatrala, University of Houston associate professor of engineering and principal investigator (PI) for the EMSL user project leading the effort. The team's results were recently published in Transport in Porous Media.
Modeling subsurface environments is particularly challenging because fluids and chemicals move through complex networks of pores and fractures, while researchers often have only limited data about what is happening underground. The EMSL–University of Houston team's method uses a type of AI called a physics-informed neural network (PINN). Unlike conventional data-driven machine learning, which learns primarily from data, a PINN is trained using both data and the scientific laws that govern fluid flow and chemical reactions underground.
As the model learns, its predictions are continually checked against those laws of physics and chemistry. If it produces a prediction that violates those rules, it adjusts its calculations until the prediction better matches both the available data and the underlying science. This approach offers several advantages over existing methods.
Traditional computer simulations can accurately model underground processes but often require significant computing resources and can be difficult to apply when important information is missing or sparsely available. Conventional AI models can make predictions more quickly, but they typically require massive amounts of training data and may produce results that are not physically realistic. By combining machine learning with established scientific principles, the team's framework can make faster predictions while remaining grounded in the physics and chemistry that control how minerals, fluids and dissolved chemicals behave underground.
Extract — continue reading at the source.
