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Found 40 papers, 7 papers with code
Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
Modern control systems are increasingly turning to machine learning algorithms to augment their performance and adaptability.
Privacy Re-identification Attacks on Tabular GANs
In doing so we also consider the situation for which re-identification attacks are formulated as reconstruction attacks, i. e., the situation where an attacker uses evolutionary multi-objective optimisation for perturbing synthetic samples closer to the training space.
Nonlinear Model Predictive Control for Enhanced Navigation of Autonomous Surface Vessels
This article proposes an approach for collision avoidance, path following, and anti-grounding of autonomous surface vessels under consideration of environmental forces based on Nonlinear Model Predictive Control (NMPC).
Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data
While RPCA offers an enhanced alternative to traditional Principal Component Analysis (PCA) for high-dimensional data management, the scope of this work extends its utilization, focusing on robust, data-driven modeling applicable to huge data sets in real-time.
Data Integration Framework for Virtual Reality Enabled Digital Twins
In this work, a data integration framework for static and real-time data from various sources on the assets and their environment is presented that allows collecting and processing of data in Python and deploying the data in real-time through Unity on different devices, including virtual reality headsets.
Modular Control Architecture for Safe Marine Navigation: Reinforcement Learning and Predictive Safety Filters
Results demonstrate the PSF's effectiveness in maintaining safety without hindering the RL agent's learning rate and performance, evaluated against a standard RL agent without PSF.
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach
Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally intractable.
Standalone, Descriptive, and Predictive Digital Twin of an Onshore Wind Farm in Complex Terrain
In this work, a digital twin with standalone, descriptive, and predictive capabilities is created for an existing onshore wind farm located in complex terrain.
CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis
In this work, we design a cascaded tabular GAN framework (CasTGAN) for generating realistic tabular data with a specific focus on the validity of the output.
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.
Demonstration of a Standalone, Descriptive, and Predictive Digital Twin of a Floating Offshore Wind Turbine
Furthermore, we demonstrate a standalone digital twin, a descriptive digital twin, and a prescriptive digital twin of an operational floating offshore wind turbine.
Deep active learning for nonlinear system identification
This simulated dataset can be used in a static deep active learning acquisition scheme referred to as global explorations.
Sparse neural networks with skip-connections for identification of aluminum electrolysis cell
Neural networks are rapidly gaining interest in nonlinear system identification due to the model's ability to capture complex input-output relations directly from data.
Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality
By understanding the capability level of a digital twin, we can better understand its potential and limitations.
An environmental disturbance observer framework for autonomous surface vessels
To investigate the capability of this observer framework, the environmental disturbances are simulated dynamically under consideration of different model and measurement uncertainties.
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control.
Sparse deep neural networks for modeling aluminum electrolysis dynamics
In this work, we demonstrate the value of sparse regularization techniques to significantly reduce the model complexity.
Prospects of federated machine learning in fluid dynamics
Physics-based models have been mainstream in fluid dynamics for developing predictive models.
Decentralized digital twins of complex dynamical systems
In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and engineering applications.
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions.
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
In the current work, we demonstrate how a hybrid approach combining the best of PBM and DDM can result in models which can outperform them both.
Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling
We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost.
Physics guided neural networks for modelling of non-linear dynamics
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention.
Nonlinear proper orthogonal decomposition for convection-dominated flows
Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space.
Ship Performance Monitoring using Machine-learning
The current work uses machine-learning (ML) methods to estimate the hydrodynamic performance of a ship using the onboard recorded in-service data.
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA) -- a novel approach to Hybrid Analysis and Modeling (HAM).
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution
Most modeling approaches lie in either of the two categories: physics-based or data-driven.
Geometric Change Detection in Digital Twins using 3D Machine Learning
Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica.
On the effectiveness of signal decomposition, feature extraction and selection on lung sound classification
A vital part of using the lung sound for disease detection is discrimination between normal lung sound and abnormal lung sound.
Sound Audio and Speech Processing
Physics guided machine learning using simplified theories
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences.
A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction
We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements.
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems.
Interface learning of multiphysics and multiscale systems
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws.
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics.
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
In this paper, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements.
Dynamical Systems Computational Physics Fluid Dynamics
A forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction
In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models.
Dynamical Systems Fluid Dynamics
Marine life through You Only Look Once's perspective
In an effort to preserve maritime wildlife the Norwegian government has decided that it is necessary to create an overview over the presence and abundance of various species of wildlife in the Norwegian fjords and oceans.
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way.
A long short-term memory embedding for hybrid uplifted reduced order models
In the first layer, we utilize an intrusive projection approach to model dynamics represented by the largest modes.
Fluid Dynamics Dynamical Systems Computational Physics
Dissecting Deep Neural Networks
It has been shown that neural networks with piecewise affine activation functions are themselves piecewise affine, with their domains consisting of a vast number of linear regions.
