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Designing microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on ...

The design of antenna systems poses a significant challenge due to stringent performance requirements dictated by contemporary applications and the high computational costs associated with models, particularly full-wave electromagnetic (EM) ...

This study explores the performance of Apple Silicon processors in real-world research tasks, with a specific focus on optimization and Machine Learning applications. Diverging from conventional benchmarks, various algorithms across fundamental ...

The importance of extracting constraints from data is emphasized by its potential practical applications in solving real-world problems. While constraints are commonly used for modeling and problem-solving, methods for learning constraints from ...

This paper deals with the restricted longest common subsequence (RLCS) problem, an extension of the well-studied longest common subsequence problem involving two sets of strings: the input strings and the restricted strings. This problem has ...

In this paper, adaptive hyperparameter optimization (HPO) strategies within the efficient global optimization (EGO) with neural network (NN)-based prediction and uncertainty (EGONN) algorithm are proposed. These strategies utilize Bayesian ...

Electronic design automation toolchains require solving various circuit manipulation problems, such as floor planning, placement and routing. These circuits may be implemented using either Very Large-Scale Integration (VLSI) or Field Programmable ...${}^{\mathrm{}}$

Non-intrusive reduced order modeling methods (ROMs) have become increasingly popular for science and engineering applications such as predicting the field-based solutions for aerodynamic flows. A large sample size is, however, required to train ...$\mathrm{}{\mathrm{}}^{\mathrm{}}$

Optimal mesh refinement is important for finite element simulations, facilitating the generation of non-uniform meshes. While existing neural network-based approaches have successfully generated high quality meshes, they can only handle a fixed ...

Solving an optimal control problem consists in finding a control structure and corresponding switching times. Unlike in a bang-bang case, switching to a singular control perturbs the control structure. The perturbation of one of the switching ...

Complete Coverage and Path Planning methods operate on many models depending on initial constraints and user demands. In this case, we optimize paths for a set of UAVs in the disaster area divided into rectangular regions of different sizes and ...

The aim of this work was to design an algorithm for rendering volumetric clouds in real time using a voxel representation. The results were verified using reference renders created with the Blender program using the Principled Volume shader. The ...

A classical non-stationary $(n+1)$-body planetary problem with n bodies of variable mass moving around the central star on quasi-elliptic orbits is considered. In addition to the mutual gravitational attraction, the bodies may be acted on by ...$\mathrm{}$

Given an optimization problem, combining knowledge from both (i) structural or algorithmic known results and (ii) new solving techniques, helps gain insight and knowledge on the aforementioned problem by tightening the gap between lower and upper ...

In this contribution, we developed a software tool for collecting information on the data traffic via control plane of an operating optical network. From this data, demand matrix elements were calculated and used to numerically estimate the edge ...

Recently proposed gradient estimators enable gradient descent over stochastic programs with discrete jumps in the response surface, which are not covered by automatic differentiation (AD) alone. Although these estimators’ capability to guide a ...

Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can significantly influence the behavior of the algorithm under consideration. Thus, proper parameter tuning should be carried out to ...

The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown power in generating content. The assessment of the usefulness of such content has become an interesting research question. Using prompt engineering, we ...

Advances in natural language processing techniques, such as named entity recognition and normalization to widely used standardized terminologies like UMLS or SNOMED-CT, along with the digitalization of electronic health records, have significantly ...

This study investigates the application of Large Language Models (LLMs) in generating commentaries on neuroscientific papers, with a focus on their stylometric differences from human-written texts. Utilizing three papers from reputable journals in ...

The ever-growing sector of wind energy underscores the importance of optimizing turbine operations and ensuring their maintenance with early fault detection mechanisms. Existing empirical and physics-based models provide approximate predictions of ...

In this paper we present a novel scientific machine learning reinterpretation of the well-known Kalman Filter, we explain its flexibility in dealing with partially-unknown models and show its effectiveness in a couple of situations where the ...

Variational Data Assimilation (DA) is a technique aimed at mitigating the error in simulated states by integrating observations. Variational DA is widely employed in weather forecasting and hydrological modeling as an optimization technique for ...

The detection and explanation of anomalies within the industrial context remains a difficult task, which requires the use of well-designed methods. In this study, we focus on evaluating the performance of Explainable Anomaly Detection (XAD) ...

In one calculation, adjoint sensitivity analysis provides the gradient of a quantity of interest with respect to all system’s parameters. Conventionally, adjoint solvers need to be implemented by differentiating computational models, which can be ...