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An Overview of Stochastic Quasi-Newton Methods for Large-Scale Machine Learning
Numerous intriguing optimization problems arise as a result of the advancement of machine learning. The stochastic first-order method is the...
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NPROS: A Not So Pure Random Orthogonal search algorithm—A suite of random optimization algorithms driven by reinforcement learning
We live in a world where waves of novel nature-inspired metaheuristic algorithms keep hitting the shore repeatedly. This never-ending surge of new...
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Multimodal Deep Learning for Manufacturing Systems: Recent Progress and Future Trends
The development of sensing technology provides large amounts and various types of data (e.g., profile, image, point cloud) to describe each stage of...
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Algebraic Machine Learning: Emphasis on Efficiency
AbstractA survey of the state of the art in research on algebraic machine learning is presented. The main emphasis is on computational complexity....
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A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by...
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Interpretable Taxonomy Extraction from Digital Assets Metadata Using Automated Unsupervised Decision Tree Learning
AbstractThis research is aimed at interpretable taxonomy extraction from digital assets metadata. The method proposed is based on automated...
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Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer’s Disease Using Biophysical Modeling and Deep Learning
Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a...
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Using Reinforcement Learning for Optimizing COVID-19 Vaccine Distribution Strategies
The COVID-19 pandemic has highlighted the critical importance of efficient and effective vaccine distribution in responding to global health...
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Optimal control by deep learning techniques and its applications on epidemic models
We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity...
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Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization
The sampling of the training data is a bottleneck in the development of artificial intelligence (AI) models due to the processing of huge amounts of...
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Advanced Machine Learning Approaches for Improving Traffic Flow Predictions in Smart Transportation Systems
Traffic flow extrapolation is a vital aspect of intellectual transportation systems, as it facilitates the smooth and efficient management of...
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Multi-agent Reinforcement Learning Aided Sampling Algorithms for a Class of Multiscale Inverse Problems
In this work, we formulate a class of multiscale inverse problems within the framework of reinforcement learning (RL) and solve it by a sampling...
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MILP Acceleration: A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound
Mixed integer linear programming (MILP) is an NP-hard problem, which can be solved by the branch and bound algorithm by dividing the original problem...
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Generating Informative Scenarios via Active Learning
Scenario generation is a crucial task in Stochastic Programming (SP). It involves a trade-off between kee** the scenario set small while making it...
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Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery
In this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we...
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CAS4DL: Christoffel adaptive sampling for function approximation via deep learning
The problem of approximating smooth, multivariate functions from sample points arises in many applications in scientific computing, e.g., in...
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Learning the flux and diffusion function for degenerate convection-diffusion equations using different types of observations
In recent years, there has been an increasing interest in utilizing deep learning-based techniques to predict solutions to various partial...
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Learning Invariance Preserving Moment Closure Model for Boltzmann–BGK Equation
As one of the main governing equations in kinetic theory, the Boltzmann equation is widely utilized in aerospace, microscopic flow, etc. Its...
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Isogeometric Topology Optimization Based on Deep Learning
Topology optimization plays an important role in a wide range of engineering applications. In this paper, we propose a novel isogeometric topology...
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A new improved teaching–learning-based optimization (ITLBO) algorithm for solving nonlinear inverse partial differential equation problems
Teaching–learning-based optimization (TLBO) algorithm is a novel population-oriented meta-heuristic algorithm. In this paper, we introduce an...
