AI Medical Compendium Topic:
Computer Simulation

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Deep compartment models: A deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling.

CPT: pharmacometrics & systems pharmacology
Nonlinear mixed effect (NLME) models are the gold standard for the analysis of patient response following drug exposure. However, these types of models are complex and time-consuming to develop. There is great interest in the adoption of machine-lear...

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics.

PLoS biology
The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to em...

Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders.

Journal of chemical theory and computation
Discovering meaningful collective variables for enhancing sampling, via applied biasing potentials or tailored MC move sets, remains a major challenge within molecular simulation. While recent studies identifying collective variables with variational...

The Synchronization Analysis of Cohen-Grossberg Stochastic Neural Networks with Inertial Terms.

Computational intelligence and neuroscience
The exponential synchronization (ES) of Cohen-Grossberg stochastic neural networks with inertial terms (CGSNNIs) is studied in this paper. It is investigated in two ways. The first way is using variable substitution to transform the system to another...

Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver.

International journal of computer assisted radiology and surgery
PURPOSE: The navigation of endovascular guidewires is a dexterous task where physicians and patients can benefit from automation. Machine learning-based controllers are promising to help master this task. However, human-generated training data are sc...

Research on Intelligent Scheduling Scheme of Aerobics Competition for Multi-Intelligent Decision-Making.

Computational intelligence and neuroscience
Multi-intelligent decision-making has a good development at present. Based on a series of technologies such as artificial intelligence, multi-intelligent decision-making is involved in many aspects, and the country also attaches great importance to t...

RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments.

Sensors (Basel, Switzerland)
Autonomous navigation in dynamic environments where people move unpredictably is an essential task for service robots in real-world populated scenarios. Recent works in reinforcement learning (RL) have been applied to autonomous vehicle driving and t...

Distributed adaptive fixed-time neural networks control for nonaffine nonlinear multiagent systems.

Scientific reports
This paper, with the adaptive backstepping technique, presents a novel fixed-time neural networks leader-follower consensus tracking control scheme for a class of nonaffine nonlinear multiagent systems. The expression of the error system is derived, ...

Passivity and Dissipativity of Fractional-Order Quaternion-Valued Fuzzy Memristive Neural Networks: Nonlinear Scalarization Approach.

IEEE transactions on cybernetics
In this article, the problem of passivity and dissipativity analysis is investigated for a class of fractional-order quaternion-valued fuzzy memristive neural networks. Based on the famous nonlinear scalarizing function, a nonlinear scalarization met...

Sideslip-Compensated Guidance-Based Adaptive Neural Control of Marine Surface Vessels.

IEEE transactions on cybernetics
This article presents an improved guidance law for underactuated marine vessels that compensates cross-track error caused by external disturbances through its sideslip. The proposed guidance law demonstrates improved path-following performance regard...