With the use of ML techniques, coupled to conventional methods like finite element and digital twin technologies, new avenues of modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective...
Here we address this issue and use machine learning methods to predict the failure of simulated two dimensional silica glasses from their initial undeformed structure. We then exploit Gradient-weighted Class Activation Mapping (Grad-CAM) to build attention maps associated with the predictions, and we...
To improve the robustness of control to hedge against the forecast uncertainties, the disjunctive data-driven uncertainty sets built upon the historical forecast errors are constructed by using machine learning methods, including principal component analysis and kernel density estimation. This work also ...
Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review
Controlling for confounding bias is crucial in causal inference. Distinct methods are currently employed to mitigate the effects of confounding bias. Each requires the introduction of a set of covariates, which remains difficult to choose, especially regarding the different methods. We conduct a simula...
An approach to the application of machine learning methods for analyzing business simulation logs is proposed, based on constructing a meta-algorithm that takes into account various types of input data. Individual player actions data are considered as action sequences and are treated by text data ...
The deluge of geoscience data also enables the successful application of machine learning and deep learning tools to deal with the complex problems in geoscience which cannot be tackled easily with traditional methods. The hierarchical design of convolutional neural network (CNN) allows it to capture...
Hybrid methods and combinations with artificial intelligence and machine learning open new possibilities as well. The ever-increasing availability of computational power and the availability of quantum computers make applications feasible that were previously beyond consideration. Simulation is pushing back the...
It breaks down the design geometries of manufactured objects into discrete, micro-level structures that have their own mathematical values. This can be applied to workflows or processes in automotive, consumer products, industrial, aerospace, marine, and much more. Manufacturing methods such as injecti...
To improve upon current methods, we propose and extensively test two light-weight physics-informed machine learning methods for online estimating the capacity of a battery cell and diagnosing its primary degradation modes using only limited early-life experimental degradation data. To enable late-life ...