scholarly journals Machine learning and predicting the time-dependent dynamics of local yielding in dry foams

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Leevi Viitanen ◽  
Jonatan R. Mac Intyre ◽  
Juha Koivisto ◽  
Antti Puisto ◽  
Mikko Alava
Keyword(s):  
Machine Learning ◽  
Time Dependent ◽  
Local Yielding

A Time-Dependent Reliability Estimation Method Based on Gaussian Process Regression

2018 ◽  
Author(s):  
Wang Han ◽  
Xiaoling Zhang ◽  
Xiesi Huang ◽  
Haiqing Li
Keyword(s):  
Machine Learning ◽  
Mechanical System ◽  
Physical Modeling ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Gaussian Process Regression ◽  
Reliability Estimation ◽  
Time Dependent ◽  
Physics Of Failure ◽  
Gear Transmission System

This paper presents a time-dependent reliability estimation method for engineering system based on machine learning and simulation method. Due to the stochastic nature of the environmental loads and internal incentive, the physics of failure for mechanical system is complex, and it is challenging to include uncertainties for the physical modeling of failure in the engineered system’s life cycle. In this paper, an efficient time-dependent reliability assessment framework for mechanical system is proposed using a machine learning algorithm considering stochastic dynamic loads in the mechanical system. Firstly, stochastic external loads of mechanical system are analyzed, and the finite element model is established. Secondly, the physics of failure mode of mechanical system at a time location is analyzed, and the distribution of time realization under each load condition is calculated. Then, the distribution of fatigue life can be obtained based on high-cycle fatigue theory. To reduce the calculation cost, a machine learning algorithm is utilized for physical modeling of failure by integrating uniform design and Gaussian process regression. The probabilistic fatigue life of gear transmission system under different load conditions can be calculated, and the time-varying reliability of mechanical system is further evaluated. Finally, numerical examples and the fatigue reliability estimation of gear transmission system is presented to demonstrate the effectiveness of the proposed method.

scholarly journals Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

2020 ◽  
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian Shelton ◽  
Bryan Wong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optimal Control ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Time Dependent ◽  
Learning Approaches ◽  
Quantum Dynamical ◽  
Quantum Dynamical Systems

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, <i>E(t)</i>, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of <i>E(t)</i>), and (2) a cross-correlation neural network approach for directly predicting <i>E(t)</i> in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

scholarly journals Special Issue on Selected Papers from IVAPP 2018

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 171
Author(s):  
Alexandru Telea ◽  
Andreas Kerren
Keyword(s):  
Machine Learning ◽  
Information Visualization ◽  
Visual Analytics ◽  
Data Science ◽  
Time Dependent ◽  
Dependent Data ◽  
Special Issue ◽  
Making Sense ◽  
Recent Developments

Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge amounts of complex,multidimensional, hybrid, and time-dependent data.[...]

Online Learning for Linear Programming with Time-Dependent Cost

2011 ◽  
Vol 230-232 ◽  
pp. 793-797
Author(s):  
Wei Li ◽  
Chong Yang Deng
Keyword(s):  
Machine Learning ◽  
Linear Programming ◽  
Online Learning ◽  
Real World ◽  
Time Dependent ◽  
Programming Models ◽  
Intelligent Optimization ◽  
Learning Parameter ◽  
Real World Problems

Machine learning and linear programming with time dependent cost are two popular intelligent optimization tools to handle uncertainty in real world problems. Thus, combining these two technologies is quite attractive. This paper proposed an effective framework to deal with uncertainty in practice, based on combing introducing learning parameter into linear programming models.

scholarly journals Discovering the Arrow of Time in Machine Learning

2021 ◽  
Author(s):  
J. Kasmire ◽  
Anran Zhao
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Anomaly Detection ◽  
Explicit Instruction ◽  
Time Dependent ◽  
Dependent Data ◽  
Temporal Context ◽  
Arrow Of Time ◽  
Twitter Data ◽  
Classification Tasks

Machine learning (ML) is increasingly useful as data grows in volume and accessibility as it can perform tasks (e.g. categorisation, decision making, anomaly detection, etc.) through experience and without explicit instruction, even when the data are too vast, complex, highly variable, full of errors to be analysed in other ways , . Thus, ML is great for natural language, images, or other complex and messy data available in large and growing volumes. Selecting a ML algorithm depends on many factors as algorithms vary in supervision needed, tolerable error levels, and ability to account for order or temporal context, among many other things. Importantly, ML methods for explicitly ordered or time-dependent data struggle with errors or data asymmetry. Most data are at least implicitly ordered, potentially allowing a hidden `arrow of time&rsquo; to affect non-temporal ML performance. This research explores the interaction of ML and implicit order by training two ML algorithms on Twitter data before performing automatic classification tasks under conditions that balance volume and complexity of data. Results show that performance was affected, suggesting that researchers should carefully consider time when selecting appropriate ML algorithms, even when time is only implicitly included.

scholarly journals Harnessing Deep Neural Networks to Solve Inverse Problems in Quantum Dynamics: Machine-Learned Predictions of Time-Dependent Optimal Control Fields

2020 ◽  
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian Shelton ◽  
Bryan Wong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Optimal Control ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Time Dependent ◽  
Learning Approaches ◽  
Quantum Dynamical ◽  
Quantum Dynamical Systems

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, <i>E(t)</i>, that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of <i>E(t)</i>), and (2) a cross-correlation neural network approach for directly predicting <i>E(t)</i> in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning approaches, particularly deep neural networks, can be employed as a cost-effective statistical approach for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

scholarly journals Time-dependent prediction of mortality and cytomegalovirus reactivation after allogeneic hematopoietic cell transplantation using machine learning

2021 ◽  
Author(s):  
Lisa Eisenberg ◽  
Christian Brossette ◽  
Jochen Rauch ◽  
Andrea Grandjean ◽  
Hellmut Ottinger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Transplantation ◽  
Hematopoietic Cell Transplantation ◽  
Hematopoietic Cell ◽  
Time Dependent ◽  
Allogeneic Hematopoietic Cell Transplantation ◽  
Individual Risk ◽  
Multiple Time ◽  
Prediction Of Mortality ◽  
Cmv Reactivation

Allogeneic hematopoietic cell transplantation (HCT) treats high-risk hematologic diseases effectively but can entail HCT-specific complications, which may be minimized by appropriate patient management and accurate, individual risk estimation. Existing clinical scores typically provide a single risk assessment before HCT and do not incorporate additional data as it becomes available. We developed machine learning models which integrate both baseline patient data and time-dependent laboratory measurements to individually predict mortality and cytomegalovirus (CMV) reactivation after HCT at multiple time points per patient. These models provide well-calibrated time-dependent risk predictions and achieved areas under the receiver-operating characteristic of 0.92 and 0.83 and areas under the precision-recall curve of 0.58 and 0.62 for prediction of mortality and CMV reactivation, respectively, in a 21-day time window. Both were successfully validated in a non-interventional, prospective study and performed on par with expert hematologists in a pilot comparison.

scholarly journals Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

2020 ◽  
Vol 22 (40) ◽  
pp. 22889-22899
Author(s):  
Xian Wang ◽  
Anshuman Kumar ◽  
Christian R. Shelton ◽  
Bryan M. Wong
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Inverse Problem ◽  
Quantum Dynamics ◽  
Deep Neural Networks ◽  
Cost Effective ◽  
Quantum Systems ◽  
Time Dependent ◽  
Learning Approach ◽  
Machine Learning Approach

Deep neural networks are a cost-effective machine-learning approach for solving the inverse problem of constructing electromagnetic fields that enable desired transitions in quantum systems.

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