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20242026
most citedAI-Driven Optimization under Uncertainty for Mineral Processing Operations

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Optimizing Lithium Production Decisions under Geological, Demand, and Pricing Uncertainties: A POMDP Framework for Multi-Objective Decision Making

Anna C. Edmonds, Mansur M. Arief, Robert J. Moss +2

Decision making in lithium production is challenging, whether from an investor's perspective or a strategic production standpoint. Determining which mines to open and when to open…

cs.AI2026

Adaptive mine planning under geological uncertainty: A POMDP framework for sequential decision-making

Hamza Khalifi, Jef Caers, Yassine Taha +2

Strategic mine production scheduling under geological uncertainty is conventionally formulated as a stochastic optimization problem in which a fixed extraction sequence and routing…

eess.SY20261 cited

AI-Driven Optimization under Uncertainty for Mineral Processing Operations

William Xu, Amir Eskanlou, Mansur Arief +2

The global capacity for mineral processing must expand rapidly to meet the demand for critical minerals, which are essential for building the clean energy technologies necessary to…

cs.AI2025

The future of AI in critical mineral exploration

Jef Caers

The energy transition through increased electrification has put the worlds attention on critical mineral exploration Even with increased investments a decrease in new discoveries h…

cs.AI2025

Managing Geological Uncertainty in Critical Mineral Supply Chains: A POMDP Approach with Application to U.S. Lithium Resources

Mansur Arief, Yasmine Alonso, CJ Oshiro +5

The world is entering an unprecedented period of critical mineral demand, driven by the global transition to renewable energy technologies and electric vehicles. This transition pr…

cs.AI2024

Intelligent prospector v2.0: exploration drill planning under epistemic model uncertainty

John Mern, Anthony Corso, Damian Burch +2

Optimal Bayesian decision making on what geoscientific data to acquire requires stating a prior model of uncertainty. Data acquisition is then optimized by reducing uncertainty on…