Multidimensional Parameter Space Research Articles

When Machine Learning Meets 2D Materials: A Review.

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Global analysis of the ALP effective theory

We perform a global fit of the effective Lagrangian for axion-like particles (ALPs) to data. By combining LHC observables from top physics, dijet and di-boson production with electroweak precision observables, we resolve the full parameter space of ALPs with flavor-universal couplings. Using the renormalization group to evolve the effective ALP couplings to low energies allows us to investigate the impact of flavor observables on the global analysis. We show that resonance searches in B → K meson decays significantly enhance the sensitivity to ALPs with sub-GeV masses. The lifetime of the ALP plays a crucial role in resolving the multi-dimensional parameter space with searches for prompt, displaced and invisible ALP decays. Our analysis points out the differences in probing an effective theory with new light particles, compared to scenarios with only non-resonant effects of heavy particles at low energies, as in the Standard Model Effective Field Theory.

Highly efficient and aberration-free off-plane grating spectrometer and monochromator for EUV—soft X-ray applications

We demonstrate a novel flat-field, dual-optic imaging EUV—soft X-ray spectrometer and monochromator that attains an unprecedented throughput efficiency exceeding 60% by design, along with a superb spectral resolution of λ/Δλ > 200 accomplished without employing variable line spacing gratings. Exploiting the benefits of the conical diffraction geometry, the optical system is globally optimized in multidimensional parameter space to guarantee optimal imaging performance over a broad spectral range while maintaining circular and elliptical polarization states at the first, second, and third diffraction orders. Moreover, our analysis indicates minimal temporal dispersion, with pulse broadening confined within 80 fs tail-to-tail and an FWHM value of 29 fs, which enables ultrafast spectroscopic and pump-probe studies with femtosecond accuracy. Furthermore, the spectrometer can be effortlessly transformed into a monochromator spanning the EUV—soft X-ray spectral region using a single grating with an aberration-free spatial profile. Such capability allows coherent diffractive imaging applications to be conducted with highly monochromatic light in a broad spectral range and extended to the soft X-ray region with minimal photon loss, thus facilitating state-of-the-art imaging of intricate nano- and bio-systems, with a significantly enhanced spatiotemporal resolution, down to the nanometer–femtosecond level.

Interactive Design-of-Experiments: Optimizing a Cooling System.

The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space. The extent of the parameter space, the complexity of the non-linear model of the system, as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other. The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation. When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.

A mathematical representation of an automated system designed to monitor critical infrastructure objects using seismic and acoustic signals

The article considers the mathematical model of the automated system of seismoacoustic monitoring of critical infrastructure objects. In the monitoring approach, the object is identified with a point in the multidimensional space of free model parameters. Thus, the forecast about the state of the object is a forecast of a significant shift in the position of the parameter vector in the parameters space. When choosing a mathematical model, it is necessary to select a space of informative parameters to reduce the probability of errors of two types. First of all, it is necessary to build a mathematical model of the dynamics of the OKI, which reflects the most significant moments of the monitoring process, including both the process itself and the disturbances accompanying this process and the noise background superimposed on the process of the dynamics of the state of the OKI. A priori knowledge of the interference of an arbitrary process will significantly weaken its influence on obtaining estimates of the parameters of the process, which is perceived as a useful signal. This attenuation is achieved by optimizing processing procedures that take into account the a priori statistics of the random interference process. A new mathematical model in the form of a superposition of Berlasi pulses in the seismoacoustic frequency range and constructive algorithms for its implementation are proposed. The mathematical properties of the model were studied. Thus, the state of OKI is reflected in the vector of free parameters of the above model. To evaluate the informative parameters of the proposed model of the automated seismo-acoustic monitoring system, the work solves the problem of nonlinear regression, considering them as the point of the criterion optimum in the n-dimensional space. In the situation that has developed in our country, related to the conduct of military operations on its territory and missile attacks, the creation of automated seismo-acoustic monitoring systems of critical infrastructure objects is a necessary task.

Scaling photonic systems-on-chip production with neural networks

We describe our use of deep learning to optimize the multi-dimensional parameter space of systems-on-chip as an important step towards the scalable production of photonic solutions and their widespread integration into high-volume applications. The challenges of transitioning between prototype and volume production are highlighted, and the suitability of deep neural networks for navigating the multi-dimensional design space of today’s photonic circuits is discussed. We adopt multi-path neural network architectures to reduce the computational requirements of model training and to mitigate the risk of overfitting. We demonstrate the use of a multi-path neural network to optimize the construction parameters of photonic designs in a high-volume production environment. Lastly, we discuss the advantages of using machine learning not only as a highly capable tool for navigating the multi-dimensional design space of complex systems-on-chip but also as an effective strategy for compensating for fabrication process non-uniformities that are undetectable by standard process metrology instruments.

Jacobian-Sensitivity Approach for Identifying Machine Dynamic Model Parameters of Robots with Flexible Joints

The versatility and large work envelope have made robots a fixture in the field of assembly for years. However, their lower stiffness and pose dependency require robust models to find optimal trajectories even for high accuracy applications. A significant obstacle in this domain is parametrizing such models of compliant robots during operation. Addressing this gap, and considering the trend of robots performing manufacturing tasks in parallel with assembly, we present an automated identification process to estimate the stiffness and damping parameters of robot joints within a milling process. This method relies solely on universally accessible kinematic chain data and force and acceleration measurements at the tool center point, eliminating the need for specialized equipment. The approach is based on a multi-body simulation, which includes flexible 6-DOF bushing joints. Key to our approach is using Jacobian-based sensitivities inside a Random Search (RS) algorithm to navigate the complexities of a sparse multi-dimensional parameter space. Our approach is versatile enough to accommodate various parameter types. We test our approach on a simulated 3-joint robot with 6 DOF per joint. By pairing the Jacobian-based sensitivities with adaptions made to the RS algorithm, we obtain accurate predictions for unknown input data with a mean relative displacement error of 2%.

(Invited) Accelerating Electrocatalysts’ Activity and Stability Assessments

The standard approach to new electrocatalysts development and testing is prohibitively slow. Moreover, it fails to meet the complexity of real devices. As a rule, it consists of routinely repeated steps, including synthesis, physicochemical characterization, and electrochemical measurements. The latter is typically based on the application of rotating disk electrodes (RDE) as conductive solid support to carry electrocatalysts and bulk electrochemical cells with liquid electrolytes as the reaction media.The current work will contain two main parts presenting our recent approaches to tackling the abovementioned limitations of the classical electrochemical test systems. As a representative example, oxygen evolution reaction (OER) electrocatalysis in electrolytes of different pH is taken. It will be shown that bearing in mind the intrinsic limitations of the aqueous mode systems (AMSs), significant acceleration of electrocatalysts’ activity and stability measurements can be achieved by using coupled techniques based on different scanning flow cells.1, 2 This approach is ideal for quick multidimensional parameter space screening. However, further tests in real systems are necessary after identifying promising candidates. It will be shown that also this step can be accelerated using model gas diffusion electrode (GDE)-based setups.3, 4 Moreover, qualitatively different information, e.g. electrocatalyst dissolution assessment, can be achieved using GDE setups coupled to external analytics like inductively coupled plasma mass spectrometry (on-line ICP-MS). While it will be shown that using two approaches, facile screening and real-device level proof tests, are feasible, limitations and challenges will also be discussed, together with further directions to develop these techniques.

Dynamical integrity estimation in time delayed systems: A rapid iterative algorithm

The robustness of dynamical systems against external perturbations is crucial in engineering; however, it is often overlooked for the lack of methods for rapidly computing it. This paper proposes a novel algorithm for estimating the robustness of systems subject to time delay. More precisely, the algorithm iteratively estimates the so-called local integrity measure (LIM), that is, the radius of the largest hypersphere centered at the fixed point and located within its basin of attraction. Since time-delayed systems are infinite dimensional, initial conditions are restricted to a constrained type of initial functions. The semi-discretization method is used for rapidly simulating the dynamics of the systems, while trajectories are classified as converging or diverging using a subdivision of the reduced phase space into cells. The algorithm was tested on four different mechanical systems, and in all cases it very quickly provided an accurate estimation of the LIM. Moreover, it enabled the study of LIM trends in a multi-dimensional parameter space, which would have been unfeasible with alternative methods. This breakthrough in computational efficiency has important implications for engineering design, allowing for careful consideration of dynamical integrity and enhancing the safety and reliability of engineered systems, especially in the presence of time delays.

Probability of rail break caused by out-of-round wheel loads

A simulation procedure to predict the probability of rail break due to a measured wheel load spectrum is presented. The load distribution includes a representative proportion of high-magnitude dynamic loads generated by out-of-round wheels. Linear elastic fracture mechanics is applied to determine the stress intensities of pre-existing rail head cracks in a continuously welded rail subjected to combined bending and temperature loading. Rail bending moments are evaluated using a validated time-domain model of dynamic vehicle–track interaction. The considered multi-dimensional stochastic parameter space includes field test data of dynamic loads from a wheel impact load detector and crack depths from eddy current data. Meta-models based on poly-harmonic splines are applied to reduce the computational cost of the analysis. Supported by the extensive field test data, the simulation procedure is demonstrated by investigating the influences of freight traffic type, track support stiffness and rail temperature on the probability of a rail break initiated at a pre-existing rail head crack.

Granular Hydrogels in Biofabrication: Recent Advances and Future Perspectives.

Granular hydrogels, which are formed by densely packing microgels, are promising materials for bioprinting due to their extrudability, porosity, and modularity. However, the multidimensional parameter space involved in granular hydrogel design makes material optimization challenging. For example, design inputs such as microgel morphology, packing density, or stiffness can influence multiple rheological properties that govern printability and the behavior of encapsulated cells. This review provides an overview of fabrication methods for granular hydrogels, and then examines how important design inputs can influence material properties associated with printability and cellular responses across multiple scales. Recent applications of granular design principles in bioink engineering are described, including the development of granular support hydrogels for embedded printing. Further, the paper provides an overview of how key physical properties of granular hydrogels can influence cellular responses, highlighting the advantages of granular materials for promoting cell and tissue maturation after the printing process. Finally, potential future directions for advancing the design of granular hydrogels for bioprinting are discussed.

A gradient set matching pursuit algorithm with anti-base mismatch ability for sparse reconstruction under low SNR

This paper is concerned with designing an effective algorithm for recovering mismatched sparse signals from noise underdetermined measurements. The sparse recovery problem is formulated as the gradient optimization model of the residual cost function in a multidimensional continuous parameter space. The mismatched atoms of the discrete dictionary matrix are recovered as the accurate target sparse coefficient by solving a gradient descent algorithm with a hyperparameters joint adaptive adjustment function. To estimate the target parameters of frequency agile radar, we propose a gradient set matching pursuit algorithm. The algorithm uses the prior knowledge of resolution of the two-dimensional ambiguity function to build a discrete dictionary and obtains the support set according to the orthogonal matching pursuit principle. We divide the gradient set space in the neighborhood of the support set and calculate the mean gradient as the judgment direction and iteration step of the initial optimization process to overcome the residual function distortion and gradient error caused by random noise. The iterative convergence process of the proposed algorithm in the support set vector space is more robust, and the problem of falling into local optimum is avoided. Compared with the conventional sparse recovery algorithm and gradient descent algorithm, the proposed algorithm boosts its reconstruction performance of target parameters in the scenario of low SNR and mismatch of multiple scattering points. Simulation results show the effectiveness of the proposed algorithm.

Rare b decays meet high-mass Drell-Yan

Rare b hadron decays are considered excellent probes of new semileptonic four-fermion interactions of microscopic origin. However, the same interactions also correct the high-mass Drell-Yan tails. In this work, we revisit the first statement in the context of this complementarity and chart the space of short-distance new physics that could show up in rare b decays. We analyze the latest b → qℓ+ℓ− measurements, where q = d or s and ℓ = e or μ, including the most recent LHCb {R}_{K^{left(ast right)}} update, together with the latest charged and neutral current high-mass Drell-Yan data, pp → ℓν and pp → ℓ+ℓ−. We implement a sophisticated interpretation pipeline within the flavio framework, allowing us to investigate the multidimensional SMEFT parameter space thoroughly and efficiently. To showcase the new functionalities of flavio, we construct several explicit models featuring either a Z′ or a leptoquark, which can explain the tension in b → sμ+μ− angular distributions and branching fractions while predicting lepton flavor universality (LFU) ratios to be SM-like, {R}_{K^{left(ast right)}}approx {R}_{K^{left(ast right)}}^{textrm{SM}} , as indicated by the recent data. Those models are then confronted against the global likelihood, including the high-mass Drell-Yan, either finding tensions or compatibility.

Probing gamma-ray burst afterglows with the Cherenkov Telescope Array

ABSTRACT Detection of delayed sub-TeV photons from gamma-ray bursts (GRBs) by MAGIC and HESS has proven the promising future of GRB afterglow studies with the Cherenkov Telescope Array (CTA), the next-generation gamma-ray observatory. With the unprecedented sensitivity of CTA, afterglow detection rates are expected to increase dramatically. In this paper, we explore the multidimensional afterglow parameter space to see the detectability of sub-TeV photons by CTA. We use a one-zone electron synchrotron and synchrotron self-Compton model to obtain the spectral energy distribution. We consider bursts going off in a medium of homogenous density. The blast wave is assumed to be radiatively inefficient and evolving adiabatically. Considering that the electron acceleration is not efficient if the acceleration time-scale exceeds the radiative cooling time-scale, we find that the sub-TeV emission is always due to the self-Compton process. We find that jets with high kinetic energy or large bulk Lorentz factor decelerating into a dense ambient medium offer better detection prospects for CTA. For relatively lower values of the downstream magnetic field, electrons are slow-cooling, and the emitted radiation is positively correlated with the magnetic field. For larger magnetic fields, the electron population enters the fast-cooling phase where the radiated flux is inversely proportional to the magnetic field. We apply our results in the context of bright TeV afterglows detected in recent years. Our results indicate that cosmological short GRBs have only moderate prospects of detection by CTA while local neutron star merger counterparts can be detected if the jet is launched towards the observer.

Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry

We use a physics-informed neural network (PINN) to simultaneously model and optimize the flow around an airfoil to maximize its lift to drag ratio. The parameters of the airfoil shape are provided as inputs to the PINN and the multidimensional search space of shape parameters is populated with collocation points to ensure that the Navier–Stokes equations are approximately satisfied throughout. We use the fact that the PINN is automatically differentiable to calculate gradients of the lift-to-drag ratio with respect to the airfoil shape parameters. This allows us to optimize with the L-BFGS gradient-based algorithm, which is more efficient than non-gradient-based algorithms, without deriving an adjoint code. We train the PINN with adaptive sampling of collocation points, such that the accuracy of the solution improves as the solution approaches the optimal point. We demonstrate this on two examples: one that optimizes a single parameter, and another that optimizes eleven parameters. The method is successful and, by comparison with conventional CFD, we find that the velocity and pressure fields have small pointwise errors and that the method converges to optimal parameters. We find that different PINNs converge to slightly different parameters, reflecting the fact that there are many closely-spaced local minima when using stochastic gradient descent. This method can be applied relatively easily to other optimization problems and avoids the difficult process of writing adjoint codes. It is, however, more computationally expensive than adjoint-based optimization. As knowledge about training PINNs improves and hardware dedicated to neural networks becomes faster, this method of simultaneous training and optimization with PINNs could become easier and faster than using adjoint codes.

Searches for heavy neutrinos at multi-TeV muon collider: a resonant leptogenesis perspective

In this work, the Standard Model (SM) is extended with two right-handed (RH) neutrinos and two singlet neutral fermions to yield active neutrino masses via (2,2) inverse see-saw mechanism. We first validate the multi-dimensional model parameter space with neutrino oscillation data, obeying the experimental bounds coming from the lepton flavor violating (LFV) decays: mu rightarrow e gamma ,~ tau rightarrow e gamma , ~ tau rightarrow mu gamma . Besides we also search for the portion of the parameter space which yield the observed baryon asymmetry of the universe via resonant leptogenesis. Further, we pick up a few benchmark points from the aforementioned parameter space with TeV scale heavy neutrinos and perform an exhaustive collider analysis of the final states: 2l + E_T !!!!!!/;, and 1,l + 2j + not ! E_T in multi-TeV muon collider.

Risk Assessment of Water Transport Enterprises by Modeling Direct and Indirect Threats

Introduction. The methods of traffic modeling by water transport and assessing the risks associated with it is needed to identify the issues of the past period, proposing methods for assessing not only direct but also indirect risks to form the preconditions for preventing them in the postwar reconstruction. The coordination of different transport type’s actions of transport requires an assessment of risks impact of the previous stages of mixed transportation on the formation of the following risks’ stages. Existing methods of assessing such impact need to be improved.
 Aim and tasks. The aim of this study is creation of methodological approach to risk management in water transport based on a mathematical model for assessing the impact of both direct and indirect risks. The tasks are: to prove that the additive approach of taking risks into account leads to the systematic deviation appearance from the result; take into account the impact on the risk of cargo transportation.
 Results. It has proved that the calculation of risk as an additive function leads to a systematic deviation from the relevant result. It stated that the risk of each of the next stages of transportation depends on the risks of the previous stages. To increase risk analysis relevance in water transport, the use of an oriented graph in a multidimensional parameter space proposed. It stated that in order to calculate the integrated risk, it is essential building not only the risk matrix but also the risk incidence matrix to take into account their relation to business entities. It established the impact of even minor risks could take the form of a catastrophe, which leads to cargo flows reorientation. It established that: for calculation of integral risk, it is crucial consider direct and indirect influences of risks; risk calculation for water transport also requires risk analysis in related modes of transport.
 Conclusions. It was established that, when calculating integral risk, it is necessary to consider direct and indirect influences on risks and that the risk calculation for water transport also requires risk analysis in related modes of transport. The proposed approach significantly increases the relevance of water transport risk analysis and allows for managing changes in transportation routes in real time.

Bayesian optimization of hydrogen plasma treatment in silicon quantum dot multilayer and application to solar cells

Silicon quantum dot multilayer (Si-QDML) is a promising material for a light absorber of all silicon tandem solar cells due to tunable bandgap energy in a wide range depending on the silicon quantum dot (Si-QD) size, which is possible to overcome the Shockley–Queisser limit. Since solar cell performance is degenerated by carrier recombination through dangling bonds (DBs) in Si-QDML, hydrogen termination of DBs is crucial. Hydrogen plasma treatment (HPT) is one of the methods to introduce hydrogen into Si-QDML. However, HPT has a large number of process parameters. In this study, we employed Bayesian optimization (BO) for the efficient survey of HPT process parameters. Photosensitivity (PS) was adopted as the indicator to be maximized in BO. PS (σp/σd) was calculated as the ratio of photoconductivity (σp) and dark conductivity (σd) of Si-QDML, which allowed the evaluation of important electrical characteristics in solar cells easily without fabricating process-intensive devices. 40-period layers for Si-QDML were prepared by plasma-enhanced chemical vapor deposition method and post-annealing onto quartz substrates. Ten samples were prepared by HPT under random conditions as initial data for BO. By repeating calculations and experiments, the PS was successfully improved from 22.7 to 347.2 with a small number of experiments. In addition, Si-QD solar cells were fabricated with optimized HPT process parameters; open-circuit voltage (VOC) and fill factor (FF) values of 689 mV and 0.67, respectively, were achieved. These values are the highest for this type of device, which were achieved through an unprecedented attempt to combine HPT and BO. These results prove that BO is effective in accelerating the optimization of practical process parameters in a multidimensional parameter space, even for novel indicators such as PS.

Parton distributions need representative sampling

In global QCD fits of parton distribution functions (PDFs), a large part of the estimated uncertainty on the PDFs originates from the choices of parametric functional forms and fitting methodology. We argue that these types of uncertainties can be underestimated with common PDF ensembles in high-stake measurements at the Large Hadron Collider and Tevatron. A fruitful approach to quantify these uncertainties is to view them as arising from sampling of allowed PDF solutions in a multidimensional parametric space. This approach applies powerful insights gained in recent statistical studies of large-scale population surveys and quasi-Monte Carlo integration methods. In particular, PDF fits may be affected by the big data paradox, which stipulates that more experimental data do not automatically raise the accuracy of PDFs---close attention to the data quality and sampling of possible PDF solutions is as essential. To test if the sampling of the PDF uncertainty of an experimental observable is truly representative of all acceptable solutions, we introduce a technique (``a hopscotch scan'') based on a combination of parameter scans and stochastic sampling. With this technique, we examine the PDF uncertainty on key LHC cross sections at 13 TeV obtained with the public NNPDF4.0 fitting code, while accounting for the likelihood distribution. We show that the uncertainties on the charm distribution at a large momentum fraction $x$ and gluon PDF at small $x$ are enlarged. In PDF ensembles obtained in the analytic minimization (Hessian) formalism, the tolerance on the PDF uncertainty must be based on sufficiently complete sampling of PDF functional forms and choices of the experiments.

Modelling the Impact of Human Population and Its Associated Pressure on Forest Biomass and Forest-Dependent Wildlife Population

Mathematical models have been widely used to explain the system originating from human-nature interaction, investigate the impacts of various components, and forecast system behaviour. This paper provides a profound reference to the current state of the art regarding the application of mathematical models to study the impact of human population and population pressure on forest biomass and forest-dependent wildlife. The review focused on two aspects, namely, model formulation and model analysis. In model formulation, the review revealed that socioeconomic status influences forest resource consumption patterns, thus, stratification of the human population based on economic status is a critical phenomenon in modelling human-nature interactions; however, this component has not been featured in the reviewed models. Regarding model analysis, in most of the reviewed work, single parameter approach was utilized to perform uncertainty quantification of the model parameter; this approach has been proven to be inadequate in measuring the uncertainty and sensitivity of the parameter. Thus, the use of correlation or variance based methods, which are multidimensional parameter space methods are of significant importance. Generally, despite the limitations of many assumptions in mathematical modelling, it is revealed that mathematical models demonstrate the ability to handle complex systems originating from interactions between humans and nature.