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Dual Variational Model of the Nonlinear Heat Conduction Problem with Consideration of Spatial Nonlocality
The microcontinuum theories have great potential for modeling structure-sensitive materials. Research into the possibilities of using nonlocal...
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Caputo derivative based nonlinear fractional order variational model for motion estimation in various application oriented spectrum
Motion information from image sequences (videos) plays a key role in solving a number of real-world problems such as surveillance, traffic...
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Federated Topic Model and Model Pruning Based on Variational Autoencoder
Topic modeling can uncover themes and patterns in large documents. However, when cross-analysis involves multiple parties, data privacy becomes a key...
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Variational Derivation of a Nonlinear Meso-Scale Model: Validating Thermophysical Properties of Nanofluids with Experimental Data
In this study, we present an exact derivation of a meso-scale mathematical model for analyzing heat transfer in nanofluids through variational...
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Variational sensitivity analysis and shape optimisation applied to a non-local, ductile damage model
Shape optimisation is applied to an elasto-plastic material model with non-local, regularised damage. Geometries of the same volume are generated...
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VESC: a new variational autoencoder based model for anomaly detection
Anomaly detection is a hot and practical problem. Most of the existing research is based on the model of the generative model, which judges...
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New Time-Integral Variational Principle
Time-integral variational principles offer a theoretical perspective and practical advantages. They provide a concept for understanding the behavior...
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A variational formulation for three-dimensional linear thermoelasticity with ‘thermal inertia’
A variational model has been developed to investigate the coupled thermo-mechanical response of a three-dimensional continuum. The linear Partial...
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Fully Variational Noise-Contrastive Estimation
By using the underlying theory of proper scoring rules, we design a family of noise-contrastive estimation (NCE) methods that are tractable for...
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Meta-variational quantum Monte Carlo
Motivated by close analogies between meta-reinforcement learning (Meta-RL) and variational quantum Monte Carlo with disorder, we propose a learning...
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Recognizing good variational quantum circuits with Monte Carlo Tree Search
Many investigators have recently turned to the study of quantum architecture search since it is laborious to manually design a high-performing...
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Constructing Nitsche’s Method for Variational Problems
Nitsche’s method is a well-established approach for weak enforcement of boundary conditions for partial differential equations (PDEs). It has many...
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Variational Circuit Based Hybrid Quantum-Classical Algorithm VC-HQCA
Quantum machine learning (QML) has emerged as a promising field that combines principles from quantum computing and machine learning. This work...
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A two-stage model for stock price prediction based on variational mode decomposition and ensemble machine learning method
Accurate stock price prediction is critical for investment decisions in the stock market. To improve the performance of stock price prediction, this...
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Prototype Two-Dimensional (2D) Problem: Variational Formulations
In this chapter, we will extend to higher dimensions the study of the prototype 1D BVP. This will lead to a new BVP based on a classical second order...
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Policy gradients using variational quantum circuits
Variational quantum circuits are being used as versatile quantum machine learning models. Some empirical results exhibit an advantage in supervised...
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Distributed algorithm for solving variational inequalities over time-varying unbalanced digraphs
In this paper, we study a distributed model to cooperatively compute variational inequalities over time-varying directed graphs. Here, each agent has...
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Variational Principles and Variational Inequalities
Two variational principles for incompressible viscoplastic fluids are derived, one for the velocity field and the stress tensor. In the absence of...
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Variational three-field reduced order modeling for nearly incompressible materials
This study presents an innovative approach for develo** a reduced-order model (ROM) tailored specifically for nearly incompressible materials at...
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Unsupervised MTS Anomaly Detection with Variational Autoencoders
MTS data often involves multiple variables or measurements recorded at each time point which poses several challenges, such as high dimensionality,...