Results 31 to 40 of about 13,256,052 (164)
AI in chemical engineering: From promise to practice
AIChE Journal, Volume 72, Issue 7, July 2026.Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, Volume 28, Issue 4, Page 1649-1672, July 2026.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
Efficient qualitative analysis of classes of recursive markov decision processes and simple stochastic games [PDF]
, 2006 . Recursive Markov Decision Processes (RMDPs) and Recursive Simple Stochastic Games (RSSGs) are natural models for recursive systems involving both probabilistic and non-probabilistic actions.
Mihalis Yannakakis +3 more
core +1 more source
Joint Chance-Constrained Markov Decision Processes
, 2022 International audienceWe consider a finite state-action uncertain constrained Markov decision process under discounted and average cost criteria. The running costs are defined by random variables and the transition probabilities are known.
Singh, Vikas, Vikram +2 more
core +1 more source
Engineering Reports, Volume 8, Issue 7, July 2026.We propose the Powerful‐but‐Limited Generative AI theorem, demonstrating that embedding human‐inspired constraints, such as fixed utility functions and neuro‐symbolic submission layers, ensures generative AI remains controllable by preventing self‐improvement beyond designer intent.
Saeed Banaeian Far +3 more
wiley +1 more source
Distributionally robust chance constrained Markov decision process with Kullback-Leibler divergence [PDF]
, 2023 This paper considers the distributionally robust chance constrained Markov decision process with random reward and ambiguous reward distribution. We consider individual and joint chance constraint cases with Kullback-Leibler divergence based ambiguity ...
Xia, Tian, Liu, Jia, Lisser, Abdel
core +1 more source
Application of Deep Learning in Virtual Power Plants—A Review
Energy Internet, Volume 3, Issue 2, Page 97-110, July 2026.ABSTRACT The advancement of deep learning (DL) has substantially improved the power grid’s ability to model and optimise diverse and highly complex tasks. Although intelligent transformation is an inevitable trend, traditional approaches increasingly reveal limitations in computational efficiency, nonlinear nonconvex optimisation, and multi‐layer ...
Hongjie Zhu +7 more
wiley +1 more source
Closing the Loop in Precision Oncology: A Digital Twin‐Driven Paradigm for Dynamic Decision‐Making
iMetaMed, Volume 2, Issue 2, June 2026.This review introduces the Closed‐Loop Intelligent Oncology System (CIOS), a five‐layer framework integrating digital twins and AI to enable adaptive, data‐driven cancer treatment. By synthesizing advances in multimodal perception, mechanistic simulation, and safe reinforcement learning, CIOS charts a roadmap toward dynamic, personalized oncology ...
Junye Zhu +3 more
wiley +1 more source
Modelling the profitability of credit cards by Markov decision processes
, 2009 This paper derives a model for the profitability of credit cards, which allow lenders to find the optimal dynamic credit limit policy. The model is a Markov decision process, where the states of the system are based on the borrower's behavioural score ...
Thomas, Lyn C., So, Mee Chi
core +1 more source
Parameterised Social Force Predictive Control for Interpretable Human Driver Modelling
IET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.To address the challenges of human‐vehicle interaction, this study develops an enhanced driver behavior model by integrating a parameterized Social Force Model with Model Predictive Control. The framework quantifies social interactions as optimization constraints, allowing the vehicle to predictively adjust its trajectory in response to dynamic ...
Yuepeng Wang +11 more
wiley +1 more source