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Altermagnets constitute a new class of magnetic materials that combine properties previously thought to be exclusive to either antiferromagnets or ferromagnets, and have unique properties of their own. In particular, a combination of symmetries connecting magnetic sublattices gives rise to a band spin splitting exhibiting unconventional d, g, or i-wave character. Their unique electronic properties have already led to new spin-dependent transport effects. Here, we consider their spin and charge dynamics on ultrafast timescales. We use a minimal tight binding model that captures the main features of the altermagnetic candidate material KRu$_4$O$_8$. In the framework of this model, we compute the spin-dependent electronic scattering dynamics after ultrashort-pulse excitation and show through these microscopic calculations how electron-electron and electron-phonon scattering processes redistribute optically excited carriers in a 2D slice of the Brillouin zone. We find that the optically excited spin polarization is long lived (~1ps) compared to the electron-electron momentum scattering lifetime of roughly 10fs. This contrasts remarkably with the much shorter spin lifetimes observed in typical ultrafast electronic spin dynamics in conventional ferromagnets and antiferromagnets, making these pulse-driven spin excitation experiments a key probe of altermagnetism.

We show that magnetic insulators with a collinear and compensated order can exhibit a thermal Hall effect even at zero magnetic field if they have altermagnetic symmetries. We predict a finite thermal Hall conductivity vector $\boldsymbol{\kappa}_\text{H}$ for a rutile-inspired effective spin model with Dzyaloshinskii-Moriya interaction. Within the linear spin-wave theory, we identify two magnon branches that carry identical Berry curvature and give rise to a finite $\boldsymbol{\kappa}_\text{H}$, which can be controlled by the N\'eel vector orientation and by strain. The thermal Hall response is further complemented with a spin Nernst response to contrast spin and heat transport in altermagnetic insulators with those in ferromagnets and antiferromagnets. Our results establish the crystal thermal Hall effect of magnons and we discuss material candidates for experimental realization, such as MnF$_2$, CoF$_2$, and NiF$_2$., Comment: 23 pages

Data is always at the center of the theoretical development and investigation of the applicability of formal concept analysis. It is therefore not surprising that a large number of data sets are repeatedly used in scholarly articles and software tools, acting as de facto standard data sets. However, the distribution of the data sets poses a problem for the sustainable development of the research field. There is a lack of a central location that provides and describes FCA data sets and links them to already known analysis results. This article analyses the current state of the dissemination of FCA data sets, presents the requirements for a central FCA repository, and highlights the challenges for this., Comment: 16 pages

The anomalous Nernst effect generates transverse voltage to the applied thermal gradient in magnetically ordered systems. The effect was previously considered excluded in compensated magnetic materials with collinear ordering. However, in the recently identified class of compensated magnetic materials, dubbed altermagnets, time-reversal symmetry breaking in the electronic band structure makes the presence of the anomalous Nernst effect possible despite the collinear spin arrangement. In this work, we investigate epitaxial Mn5Si3 thin films known to be an altermagnetic candidate. We show that the material manifests a sizable anomalous Nernst coefficient despite the small net magnetization of the films. The measured magnitudes of the anomalous Nernst coefficient reach a scale of microVolts per Kelvin. We support our magneto-thermoelectric measurements by density-functional theory calculations of the material's spin-split electronic structure, which allows for the finite Berry curvature in the reciprocal space. Furthermore, we present our calculations of the intrinsic Berry-curvature Nernst conductivity, which agree with our experimental observations.

We present a phenomenological theory of altermagnets, that captures their unique magnetization dynamics and allows modelling magnetic textures in this new magnetic phase. Focusing on the prototypical d-wave altermagnets, e.g. RuO$_2$, we can explain intuitively the characteristic lifted degeneracy of their magnon spectra, by the emergence of an effective sublattice-dependent anisotropic spin stiffness arising naturally from the phenomenological theory. We show that as a consequence the altermagnetic domain walls, in contrast to antiferromagnets, have a finite gradient of the magnetization, with its strength and gradient direction connected to the altermagnetic anisotropy, even for 180$^\circ$ domain walls. This gradient generates a ponderomotive force in the domain wall in the presence of a strongly inhomogeneous external magnetic field, which may be achieved through magnetic force microscopy techniques. The motion of these altermagentic domain walls is also characterized by an anisotropic Walker breakdown, with much higher speed limits of propagation than ferromagnets but lower than antiferromagnets.

The online implementation of model predictive control for constrained multivariate systems has two main disadvantages: it requires an estimate of the entire model state and an optimisation problem must be solved online. These issues have typically been treated separately. This work proposes an integrated approach for the offline training of an output feedback neural network controller in closed loop. Online this neural network controller computers the plant inputs cheaply using noisy measurements. In addition, the controller can be trained to only make use of certain predefined measurements. Further, a heuristic approach is proposed to perform the automatic selection of important measurements. The proposed method is demonstrated by extensive simulations using a non-linear distillation column model of 50 states.

Plastic-scintillator detectors are devices used for the detection of elementary particles. They provide good particle identification with excellent time resolution, whilst being inexpensive due to the affordability of plastic materials. Particle tracking is achieved by segmenting the scintillator into smaller optically-isolated 3D granular sub-structures which require the integration of multiple types of plastic materials as well as several thousands of tiny holes through a compact volume of several cubic meters. Future particle detectors necessitate larger volumes, possibly with even finer segmentation. However, manufacturing such geometries with current production strategies is challenging, as they involve time-consuming and costly fabrication processes, followed by the assembly of millions of individual parts. The difficulty in scaling up such a workflow can be addressed by additive manufacturing, enabling the construction of complex, monolithic geometries in a single operation. This article presents the fabrication of the first additive manufactured plastic scintillator detector, capable of 3D tracking elementary particles and measuring their stopping power. Its performance is comparable to the state of the art of plastic scintillator detectors. This work paves the way towards a new feasible, time and cost-effective process for the production of future plastic-based scintillator detectors, regardless their size and difficulty in geometry.

For large systems that consider uncertainty the online solution of model predictive control problems can be computationally taxing, and even infeasible. This can be offset by using a shorter horizon, however this can in turn result in poor controller performance. In this work we consider the task of learning convex objective terms to allow the use of a short, potentially single step, control horizon to reduce the online computational cost. We consider two surrogates: (1) a convex interpolating function and (2) an input-convex neural network. We highlight that irrespective of the choice of surrogate the behaviour of the surrogate near the origin and ability of the surrogate to describe the feasible region are key concerns for the closed loop performance of the new MPC problem. We address these concerns by tailoring the design of the surrogate to ensure good performance in both aspects.The paper concludes with a numerical example, showing the clear and significant reduction in computational complexity through the use of these convex surrogates.

Topological semimetal antiferromagnets provide a rich source of exotic topological states which can be controlled by manipulating the orientation of the N\'eel vector, or by modulating the lattice parameters through strain. We investigate via ${ab\ initio}$ density functional theory calculations, the effects of shear strain on the bulk and surface states n two antiferromagnetic EuCd$_2$As$_2$ phases with out-of-plane and in-plane spin configurations. When magnetic moments are along the $\textit{c}$-axis, a $3\%$ longitudinal or diagonal shear strain can tune the Dirac semimetal phase to an axion insulator phase, characterized by the parity-based invariant $\eta_{4I} = 2$. For an in-plane magnetic order, the axion insulator phase remains robust under all shear strains. We further find that for both magnetic orders, the bulk gap increases and a surface gap opens on the (001) surface up to 16 meV. Because of a nonzero $\eta_{4I}$ index and gapped states on the (001) surface, hinge modes are expected to happen on the side surface states between those gapped surface states. This result can provide a valuable insight in the realization of the long-sought axion states., Comment: 5 pages, 4 figures

Altermagnetism represents an emergent collinear magnetic phase with compensated order and an unconventional alternating even-parity wave spin order in the non-relativistic band structure. We investigate directly this unconventional band splitting near the Fermi energy through spinintegrated soft X-ray angular resolved photoemission spectroscopy. The experimentally obtained angle-dependent photoemission intensity, acquired from epitaxial thin films of the predicted altermagnet CrSb, demonstrates robust agreement with the corresponding band structure calculations. In particular, we observe the distinctive splitting of an electronic band on a low-symmetry path in the Brilliouin zone that connects two points featuring symmetry-induced degeneracy. The measured large magnitude of the spin splitting of approximately 0.6 eV and the position of the band just below the Fermi energy underscores the signifcance of altermagnets for spintronics based on robust broken time reversal symmetry responses arising from exchange energy scales, akin to ferromagnets, while remaining insensitive to external magnetic fields and possessing THz dynamics, akin to antiferromagnets., Comment: 13 pages, 9 figures (including supplementary information)

The p-wave Cooper-pairing instability in superfluid $^{3}$He, characterized by a parity-breaking excitation gap, is regarded as one of the most rich and complex phenomena in physics. The possibility of a counterpart unconventional p-wave ordering of interacting fermions, in which a Fermi surface spontaneously breaks the parity symmetry, has been an open problem for many decades. Here we identify the realization of the counterpart of p-wave superfluidity in magnetism. We demonstrate a strong parity-breaking and anisotropic symmetry lowering of spin-polarized and time-reversal symmetric Fermi surfaces in a representative p-wave magnet CeNiAsO. As a direct experimental signature we predict a large spontaneous anisotropy of the resistivity. Abundant and robust realizations of the unconventional p-wave magnetism can be identified from suitable non-relativistic crystal-lattice and spin symmetries, without requiring strong correlations and extreme external conditions. This opens new prospects in fields ranging from topological phenomena to spintronics., Comment: 6 pages, 3 figures

Altermagnets are compensated magnets with unconvetional $d$, $g$ and $i$-wave spin-channel order in reciprocal space. So far the search for new altermagnetic candidates has been focused on materials in which the magnetic unit cell is identical to the non-magnetic one, i.e. magnetic structures with zero propagation vector. Here, we substantially broaden the family of altermagnetic candidates by predicting supercell altermagnets. Their magnetic unit cell is constructed by enlarging the paramagnetic primitive unit cell, resulting in a non-zero propagation vector for the magnetic structure. This connection of the magnetic configuration to the ordering of sublattices gives an extra degree of freedom to supercell altermagnets, which can allow for the control over the order parameter spatial orientation. We identify realistic candidates MnSe$_2$ with a $d$-wave order, and RbCoBr$_3$, CsCoCr$_3$, and BaMnO$_3$ with $g$-wave order. We demonstrate the reorientation of the order parameter in MnSe$_2$, which has two different magnetic configurations, whose energy difference is only 5 meV, opening the possibility of controlling the orientation of the altermagnetic order parameter by external perturbations., Comment: 10 pages, 4 figures

This paper presents an adaptive horizon multi-stage model-predictive control (MPC) algorithm. It establishes appropriate criteria for recursive feasibility and robust stability using the theory of input-to-state practical stability (ISpS). The proposed algorithm employs parametric nonlinear programming (NLP) sensitivity and terminal ingredients to determine the minimum stabilizing prediction horizon for all the scenarios considered in the subsequent iterations of the multi-stage MPC. This technique notably decreases the computational cost in nonlinear model-predictive control systems with uncertainty, as they involve solving large and complex optimization problems. The efficacy of the controller is illustrated using three numerical examples that illustrate a reduction in computational delay in multi-stage MPC., Comment: Accepted for publication in Elsevier's Journal of Process Control

We use sensitivity analysis to design $\textit{optimality-based}$ discretization (cutting-plane) methods for the global optimization of nonconvex semi-infinite programs (SIPs). We begin by formulating the optimal discretization of SIPs as a max-min problem and propose variants that are more computationally tractable. We then use parametric sensitivity theory to design an efficient method for solving these max-min problems to local optimality and argue this yields valid discretizations without sacrificing global optimality guarantees. Finally, we formulate optimality-based $\textit{generalized}$ discretization of SIPs as max-min problems and design efficient local optimization algorithms to solve them approximately. Numerical experiments on test instances from the literature demonstrate that our new optimality-based discretization methods can significantly reduce the number of iterations for convergence relative to the classical feasibility-based method.

To operate process engineering systems in a safe and reliable manner, predictive models are often used in decision making. In many cases, these are mechanistic first principles models which aim to accurately describe the process. In practice, the parameters of these models need to be tuned to the process conditions at hand. If the conditions change, which is common in practice, the model becomes inaccurate and needs to be re-tuned. In this paper, we propose a hybrid modeling machine learning framework that allows tuning first principles models to process conditions using two different types of Bayesian Neural Networks. Our approach not only estimates the expected values of the first principles model parameters but also quantifies the uncertainty of these estimates. Such an approach of hybrid machine learning modeling is not yet well described in the literature, so we believe this paper will provide an additional angle at which hybrid machine learning modeling of physical systems can be considered. As an example, we choose a multiphase pipe flow process for which we constructed a three-phase steady state model based on the drift-flux approach which can be used for modeling of pipe and well flow behavior in oil and gas production systems with or without the neural network tuning. In the simulation results, we show how uncertainty estimates of the resulting hybrid models can be used to make better operation decisions., Comment: 20 pages, 10 figures, not published in any journal

We propose a new interconnection relation for infinite-dimensional port-Hamiltonian systems that enables the coupling of ports with different spatial dimensions by integrating over the the surplus dimensions. To show the practical relevance, we apply this interconnection to a model system of an actively cooled gas turbine blade. We also show that this interconnection relation behaves well with respect to a discretization in finite element space, ensuring its usability for practical applications., Comment: 6 pages, 1 figure. Submitted to Applied Mathematics Letters

Antennas for wireless body area networks (WBAN) need to be modeled with adapted methods because the coupling with the body tissue does not allow for a clear separation between antenna and channel. Especially for dynamically varying on-body channels due to changing body poses, e.g. with head-worn antennas, modeling is challenging and design goals for optimal antennas are difficult to determine. Therefore, in this paper, the modeling of WBAN channels using spherical wave functions (SWF) is utilized for antenna de-embedding and for deriving optimal antenna characteristics that maximize the transmission coefficient for the respective channel. It is evaluated how typical factors influencing WBAN channels (different body anatomies, body postures, and varying positions of the communication nodes), can be modeled statistically with SWF. An optimized antenna design is developed based on the derived optimization method, specifically adapted to the channel of on-body links with eye-wear applications. The results with the optimized antenna are compared to other standard antenna designs and validated against measurements.

Neural differential equations have recently emerged as a flexible data-driven/hybrid approach to model time-series data. This work experimentally demonstrates that if the data contains oscillations, then standard fitting of a neural differential equation may result in a flattened out trajectory that fails to describe the data. We then introduce the multiple shooting method and present successful demonstrations of this method for the fitting of a neural differential equation to two datasets (synthetic and experimental) that the standard approach fails to fit. Constraints introduced by multiple shooting can be satisfied using a penalty or augmented Lagrangian method., Comment: in IEEE Control Systems Letters

We study the microscopical origin of anisotropic ferromagnetism in the van der Waals magnet CrI3. We conclude that the nearest neighbors exchange is well described by the Heisenberg-Kitaev-Gamma (HKGamma) model, and we also found a nonzero Dzyaloshinskii-Moriya interaction (DMI) on next nearest neighbors. Both Kitaev and DMI are known to generate a non-trivial topology of the magnons in the honeycomb lattice and have been used separately to describe the low energy regime of this material. We discuss that including one or the other leads to different signs of the Chern number. Furthermore, the topological gap at K-point seems to be mainly produced by DMI, despite it is one order of magnitude smaller than Kitaev. Finally, we show that by applying an external electric field perpendicular to the crystal plane, it is possible to induce DMI on nearest neighbors, and this could have consequences in non-collinear spin textures, such as domain walls and skyrmions., Comment: Submitted to Physical Review B. 13 pages and 7 figures; Added references in section II.C

This paper describes the project GivEn that develops a novel multicriteria optimization process for gas turbine blades and vanes using modern "adjoint" shape optimization algorithms. Given the many start and shut-down processes of gas power plants in volatile energy grids, besides optimizing gas turbine geometries for efficiency, the durability understood as minimization of the probability of failure is a design objective of increasing importance. We also describe the underlying coupling structure of the multiphysical simulations and use modern, gradient based multicriteria optimization procedures to enhance the exploration of Pareto-optimal solutions.

In this article, we calculate the magnon spectrum of Kitaev-Heisenberg magnets. This model has been recently proposed as a spin Hamiltonian to model $\text{CrI}_3$ and other two-dimensional magnets. It is a minimal spin Hamiltonian that includes a contribution stemming from a Heisenberg, isotropic exchange, and a contribution arising from a Kitaev interaction, anisotropic and frustrated exchange. Our calculations reveal the topological nature of the magnons and a gap that opens at the $K$ and $K^\prime$ points. These topological properties give rise to effects such as thermal Hall effect. In addition to the bulk properties, we calculate the magnon spectrum of nanoribbons illustrating the corresponding edge states., Comment: 5 pages, 4 figures

This paper proposes a shape optimization algorithm based on the principles of Isogeometric Analysis (IGA) in which the parameterization of the geometry enters the problem formulation as an additional PDE-constraint. Inspired by the isoparametric principle of IGA, the parameterization and the governing state equation are treated using the same numerical technique. This leads to a scheme that is comparatively easy to differentiate, allowing for a fully symbolic derivation of the gradient and subsequent gradient-based optimization. To improve the efficiency and robustness of the scheme, the basis is re-selected during each optimization iteration and adjusted to the current needs. The scheme is validated in two test cases.

Vossian Antonomasia is a prolific stylistic device, in use since antiquity. It can compress the introduction or description of a person or another named entity into a terse, poignant formulation and can best be explained by an example: When Norwegian world champion Magnus Carlsen is described as "the Mozart of chess", it is Vossian Antonomasia we are dealing with. The pattern is simple: A source (Mozart) is used to describe a target (Magnus Carlsen), the transfer of meaning is reached via a modifier ("of chess"). This phenomenon has been discussed before (as 'metaphorical antonomasia' or, with special focus on the source object, as 'paragons'), but no corpus-based approach has been undertaken as yet to explore its breadth and variety. We are looking into a full-text newspaper corpus (The New York Times, 1987-2007) and describe a new method for the automatic extraction of Vossian Antonomasia based on Wikidata entities. Our analysis offers new insights into the occurrence of popular paragons and their distribution.

Spin noise spectroscopy has developed into a very powerful tool to access the electron spin dynamics. While the spin-noise power spectrum in an ensemble of quantum dots in a magnetic field is essentially understood, we argue that the investigation of the higher order cumulants promises to provide additional information not accessible by the conventional power noise spectrum. We present a quantum mechanical approach to the correlation function of the spin-noise power operators at two different frequencies for small spin bath sizes and compare the results with a simulation obtained from the classical spin dynamics for large number of nuclear spins. This bispectrum is defined as a two-dimensional frequency cut in the parameter space of the fourth-order spin correlation function. It reveals information on the influence of the nuclear-electric quadrupolar interactions on the long-time electron spin dynamics dominated by a magnetic field. For large bath sizes and spin lengths the quantum mechanical spectra converge to those of the classical simulations. The broadening of the bispectrum across the diagonal in the frequency space is a direct measure of the quadrupolar interaction strength. A narrowing is found with increasing magnetic field indicating a suppression of the influence of quadrupolar interactions in favor of the nuclear Zeeman effect., Comment: 15 pages, 14 figures