Large Parameter Space Research Articles

2D freeform topology optimization in the real and spectral domains using the slime mold algorithm

The photonics community has shown increasing interest in the inverse design of photonic components and devices using gradient-based methods. This is due to its efficient gradient computation and suitability for large parameter and continuous design spaces when it is combined with the adjoint method. Despite their strong potential and ability to escape local minima, metaheuristic methods remain underutilized, particularly in photonics for topology optimization. The slime mold algorithm (SMA) is an advanced metaheuristic method that has proven its effectiveness in designing 1D structures. In this paper, we extend its application to the inverse design of 2D freeform structures. Two strategies are proposed and compared in this work: the first strategy involves using the values in the real domain of the permittivity tensor components as design variables. The second, more innovative strategy, involves optimizing the design through the Fourier coefficients of the components of the permittivity tensor. This latter method, which aligns better with the Fourier modal method (FMM) and spectral methods in general, reduces the dimensionality of the optimization problem.

GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance

In this paper, we present GaussianPainter, the first method to paint a point cloud into 3D Gaussians given a reference image. GaussianPainter introduces an innovative feed-forward approach to overcome the limitations of time-consuming test-time optimization in 3D Gaussian splatting. Our method addresses a critical challenge in the field: the non-uniqueness problem inherent in the large parameter space of 3D Gaussian splatting. This space, encompassing rotation, anisotropic scales, and spherical harmonic coefficients, introduces the challenge of rendering similar images from substantially different Gaussian fields. As a result, feed-forward networks face instability when attempting to directly predict high-quality Gaussian fields, struggling to converge on consistent parameters for a given output. To address this issue, we propose to estimate a surface normal for each point to determine its Gaussian rotation. This strategy enables the network to effectively predict the remaining Gaussian parameters in the constrained space. We further enhance our approach with an appearance injection module, incorporating reference image appearance into Gaussian fields via a multiscale triplane representation. Our method successfully balances efficiency and fidelity in 3D Gaussian generation, achieving high-quality, diverse, and robust 3D content creation from point clouds in a single forward pass. A video is provided in our supplementary material for a more detailed explanation of our method.

Displaced heavy neutral lepton from new Higgs doublet

Heavy neutral leptons N are introduced to explain the tiny neutrino masses via the seesaw mechanism. For proper small mixing parameter VℓN, the heavy neutral leptons N become long-lived, which leads to the displaced vertex signature at colliders. In this paper, we consider the displaced heavy neutral lepton from the neutrinophilic Higgs doublet Φν decay. The new Higgs doublet with MeV scale VEV can naturally explain the tiny neutrino masses with TeV scale N. Different from current experimental searches via the W±→ℓ±N decay, the branching ratios of new decays as H±→ℓ±N are not suppressed when |VℓN|≳10-16. Therefore, a larger parameter space is expected to be detected at colliders. We then investigate the promising region at the 14 TeV HL-LHC and the 3 TeV CLIC. According to our simulation, the DV signature could probe |VℓN|2≳10-18 with mN<mH+, which covers the seesaw predicted value |VℓN|2∼mν/mN. We could probe mH+≲1100 GeV at the 14 TeV HL-LHC and mH+≲1490 GeV at the 3 TeV CLIC.

Transient Thermal Energy Harvesting at a Single Temperature Using Nonlinearity

The authors present an in-depth theoretical study of two nonlinear circuits capable of transient thermal energy harvesting at one temperature. The first circuit has a storage capacitor and diode connected in series. The second circuit has three storage capacitors, and two diodes arranged for full wave rectification. The authors solve both Ito–Langevin and Fokker–Planck equations for both circuits using a large parameter space including capacitance values and diode quality. Surprisingly, using diodes one can harvest thermal energy at a single temperature by charging capacitors. However, this is a transient phenomenon. In equilibrium, the capacitor charge is zero, and this solution alone satisfies the second law of thermodynamics. The authors found that higher quality diodes provide more stored charge and longer lifetimes. Harvesting thermal energy from the ambient environment using diode nonlinearity requires capacitors to be charged but then disconnected from the circuit before they have time to discharge.

The atmospheric entry of cometary impactors

ABSTRACT Cometary impacts play an important role in the early evolution of Earth, and other terrestrial exoplanets. Here, we present a numerical model for the interaction of weak, low-density cometary impactors with planetary atmospheres, which includes semi-analytical parametrizations for the ablation, deformation, and fragmentation of comets. Deformation is described by a pancake model, as is appropriate for weakly cohesive, low-density bodies, while fragmentation is driven by the growth of Rayleigh–Taylor instabilities. The model retains sufficient computational simplicity to investigate cometary impacts across a large parameter space, and permits simple description of the key physical processes controlling the interaction of comets with the atmosphere. We apply our model to two case studies. First, we consider the cometary delivery of prebiotic feedstock molecules. This requires the survival of comets during atmospheric entry, which is determined by three parameters: the comet’s initial radius, bulk density, and atmospheric surface density. There is a sharp transition between the survival and catastrophic fragmentation of comets at a radius of about 150 m, which increases with increasing atmospheric surface density and decreasing cometary density. Second, we consider the deposition of mass and kinetic energy in planetary atmospheres during cometary impacts, which determines the strength and duration of any atmospheric response. We demonstrate that mass loss is dominated by fragmentation, not ablation. Small comets deposit their entire mass within a fraction of an atmospheric scale height, at an altitude determined by their initial radius. Large comets lose only a small fraction of their mass to ablation in the lower atmosphere.

Biomimetic stimulation patterns drive natural artificial touch percepts using intracortical microstimulation in humans.

Intracortical microstimulation (ICMS) of human somatosensory cortex evokes tactile percepts that people describe as originating from their own body, but are not always described as feeling natural. It remains unclear whether stimulation parameters such as amplitude, frequency, and spatiotemporal patterns across electrodes can be chosen to increase the naturalness of these artificial tactile percepts. In this study, we investigated whether biomimetic stimulation patterns - ICMS patterns that reproduce essential features of natural neural activity - increased the perceived naturalness of ICMS-evoked sensations compared to a non-biomimetic pattern in three people with cervical spinal cord injuries. All participants had electrode arrays implanted in their somatosensory cortices. Rather than qualitatively asking which pattern felt more natural, participants directly compared natural residual percepts, delivered by mechanical indentation on a sensate region of their hand, to artificial percepts evoked by ICMS and were asked whether linear non-biomimetic or biomimetic stimulation felt most like the mechanical indentation. We show that simple biomimetic ICMS, which modulated the stimulation amplitude on a single electrode, was perceived as being more like a mechanical indentation reference on 32% of the electrodes. We also tested an advanced biomimetic stimulation scheme that captured more of the spatiotemporal dynamics of cortical activity using co-modulated stimulation amplitudes and frequencies across four electrodes. Here, ICMS felt more like the mechanical reference for 75% of the electrode groups. Finally, biomimetic stimulus trains required less charge than their non-biomimetic counterparts to create an intensity-matched sensation. We conclude that ICMS encoding schemes that mimic naturally occurring neural spatiotemporal activation patterns in the somatosensory cortex feel more like an actual touch than non-biomimetic encoding schemes. This also suggests that using key elements of neuronal activity can be a useful conceptual guide to constrain the large stimulus parameter space when designing future stimulation strategies. This work is a part of Clinical Trial NCT01894802.

Statistical Design of Experiments Enables Rapid Exploration of Perfluorobutane Sulfonate Degradation

The phase-out of long-chain PFAS has led to the proliferation of highly recalcitrant short-chain analogs, and mineralization technologies for these short-chain PFAS are needed to mitigate their deleterious effects on environmental and human health. In this study, we utilize a statistical design of experiments, specifically, response surface methodology, to rapidly evaluate the electrochemical degradation of the short-chain PFAS, perfluorobutane sulfonate (PFBS). We evaluate the impacts of the three primary electrochemical parameters (concentration of PFBS, concentration of supporting electrolyte, and applied current) over multiple orders of magnitude on the three primary reaction outcomes of electrochemical PFBS degradation (incomplete PFBS decomposition, complete PFBS mineralization as fluorine, and anodic energy consumption). Our results correspond with literature and clearly identify the well-known tradeoff between energy consumption and complete mineralization. Intriguingly, partial PFBS decomposition and energy consumption demonstrate nonlinear dependencies in the current/supporting electrolyte concentration space and the current/PFBS concentration space, respectively. These findings highlight the utility of the response surface methodology model to efficiently interrogate a large parameter space, identifying both common results and less-obvious interactions between electrochemical parameters and their influences on reaction outcomes.

High-throughput numerical modeling of the tunable synaptic behavior in 2D MoS2 memristive devices

Memristive devices based on two-dimensional (2D) materials have emerged as potential synaptic candidates for next-generation neuromorphic computing hardware. Here, we introduce a numerical modeling framework that facilitates efficient exploration of the large parameter space for 2D memristive synaptic devices. High-throughput charge-transport simulations are performed to investigate the voltage pulse characteristics for lateral 2D memristors and synaptic device metrics are studied for different weight-update schemes. We show that the same switching mechanism can lead to fundamentally different pulse characteristics influencing not only the device metrics but also the weight-update direction. A thorough analysis of the parameter space allows simultaneous optimization of the linearity, symmetry, and drift in the synaptic behavior that are related through tradeoffs. The presented modeling framework can serve as a tool for designing 2D memristive devices in practical neuromorphic circuits by providing guidelines for materials properties, device functionality, and system performance for target applications.

A fast and robust recipe for modeling nonideal magnetohydrodynamic effects in star formation simulations

Nonideal magnetohydrodynamic (MHD) effects are thought to be a crucial component of the star formation process. Numerically, several complications render the study of nonideal MHD effects in 3D simulations extremely challenging and hinder efforts to explore a large parameter space. Here, we aim to overcome such challenges by proposing a novel physically motivated empirical approximation to model nonideal MHD effects. We performed a number of 2D axisymmetric three-fluid nonideal MHD simulations of collapsing prestellar cores and clouds with non-equilibrium chemistry and leveraged previously published results from similar simulations with different physical conditions. We utilized these simulations to develop a multivariate interpolating function that predicts the ionization fraction in each region of the cloud depending on the local physical conditions. We subsequently used analytically derived simplified expressions to calculate the resistivities of the cloud in each grid cell. Therefore, in our new approach, the resistivities are calculated without the use of a chemical network. We benchmarked our method against additional 2D axisymmetric nonideal MHD simulations with random initial conditions and a 3D nonideal MHD simulation with non-equilibrium chemistry. We find excellent quantitative and qualitative agreement between our approach and the ``full'' nonideal MHD simulations both in terms of the spatial structure of the simulated clouds and regarding their time evolution. At the same time, we achieved a factor of ∼10^2-10^3 increase in computational speed. Given that we ignored the contribution of grains to the resistivities our approximation is valid up to number densities of ∼10^6 cm^ and is therefore suitable for parsec-scale simulations of molecular clouds and/or simulations of stratified boxes.

Constraints on Cosmic-Ray Acceleration in Bright Gamma-Ray Bursts with Observations of Fermi

Abstract Gamma-ray bursts (GRBs) are widely suggested as potential sources of ultrahigh-energy cosmic rays. The kinetic energy of the jets dissipates, leading to the production of an enormous amount of gamma-ray photons and possibly also the acceleration of protons. The accelerated protons will interact with the radiation of the GRB via the photomeson and Bethe–Heitler processes, which can initiate electromagnetic cascades. This process can give rise to broadband radiation up to the GeV–TeV gamma-ray regime. The expected gamma-ray flux from cascades depends on the properties of the GRB jet, such as the dissipation radius R diss, the bulk Lorentz factor Γ, and the baryon loading factor η p . Therefore, observations of Fermi-LAT can impose constraints on these important parameters. In this study, we select 12 GRBs of high keV–MeV fluence and constrain the baryon loading factor, under different combinations of the bulk Lorentz factor and the dissipation radius based on Fermi-LAT's measurements. Our findings indicate a strong constraint of η p &lt; 10 for most selected GRBs over a large parameter space except for large dissipation radii (≳1015 cm) and high bulk Lorentz factors (≳600). The constraint is comparable to, and in some GRBs even stronger than, that from high-energy neutrinos for stacked GRBs. Our results suggest that for a typical bulk Lorentz factor of several hundred, the dissipation radii of GRBs need to be large to avoid overshooting the GeV gamma-ray flux during the prompt emission phase of GRBs, which can be used to constrain GRBs.

Using deep learning to design high aspect ratio fusion devices

The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance criteria. However, optimization is still required to find configurations with properties such as low elongation, high rotational transform, finite beta and good fast particle confinement. In this work, we train a machine learning model to construct configurations with favourable confinement properties by finding a solution to the inverse design problem, that is, obtaining a set of model input parameters for given desired properties. Since the solution of the inverse problem is non-unique, a probabilistic approach, based on mixture density networks, is used. It is shown that optimized configurations can be generated reliably using this method.

QCD-sourced tachyonic phase transition in a supercooled Universe

We propose a novel gravitational wave production mechanism in the context of quasi-conformal Standard Model extensions, which provide a way to dynamically generate the electroweak scale. In these models, the cosmic thermal history is modified by a substantial period of thermal inflation, potentially supercooling the Universe below the QCD scale. The exit from supercooling is typically realized through a strong, first-order phase transition. By employing the classically conformal U(1)B-L model as a representative example, we show that a large parameter space exists where bubble percolation is inefficient. In this case, the top quark condensate triggers a tachyonic phase transition driven by classical rolling of the new scalar field towards the true vacuum. As the field crosses a region where its effective mass is negative, long-wavelength scalar field fluctuations are exponentially amplified, preheating the supercooled Universe. We study the dynamics of this scenario and estimate the peak of the associated gravitational wave signal, which is detectable by future observatories in almost the entire available parameter space.

The Pareto effect in tipping social networks: from minority to majority

Abstract. How do social networks tip? A popular theory is that a small minority can trigger population-wide social change. This aligns with the Pareto principle, a semi-quantitative law which suggests that, in many systems, 80 % of effects arise from 20 % of the causes. In the context of the transition to net-zero emissions, this vital 20 % can be a critical instigator of social tipping, a process which can rapidly change social norms. In this work, we asked whether the Pareto effect can be observed in social systems by conducting a literature review, placing a focus on social norm diffusion and complex contagion via social networks. By analysing simulation and empirical results of social tipping events across disciplines and a large parametric space, we identified consistent patterns across studies and key factors which help or hinder social tipping. We show evidence supporting a tipping point near 25 % of the total population within our compiled dataset. Near this critical mass, we observe a high likelihood for a social tipping event, where a large majority quickly adopts new norms. Our findings illustrate slight variations between modelling and empirical results, with average tipping points at 24 % and 27 %, respectively. Additionally, we show a range of critical masses where social tipping is possible; these values lie between 10 % and 43 %. These results indicate the potential, but not the inevitability, of rapid social change in certain susceptible populations and contexts. Finally, we provide practical guidance for facilitating difficult norm changes by (1) leveraging trusted community structures and building critical mass in clustered networks (particularly in the 10 %–43 % threshold range), (2) adapting strategies based on norm type and context, and (3) targeting groups with moderate preferences and network positions – avoiding reliance on highly central or well-connected individuals – to enable endogenous spread.

Indirect searches for ultraheavy dark matter in the time domain

Dark matter may exist today in the form of ultraheavy composite bound states. Collisions between such dark matter states can release intense bursts of radiation that include γ-rays among the final products. Thus, indirect detection signals of dark matter may include unconventional γ-ray bursts. Such bursts may have been missed not necessarily because of their low arriving γ-ray fluxes, but rather their briefness and rareness. We point out that intense bursts whose nondetection thus far are due to the latter can be detected in the near future with existing and planned facilities. In particular, we propose that, with slight experimental adjustments and suitable data analyses, imaging atmospheric Cherenkov telescopes (IACTs) and Pulsed All-Sky Near-Infrared and Optical Search for Extra-Terrestrial Intelligence (PANOSETI) are promising tools for detecting such rare, brief, but intense bursts. We also show that, if we assume these bursts originate from collisions of dark matter states, IACTs and PANOSETI can probe a large dark matter parameter space beyond existing limits. Additionally, we present a concrete model of dark matter that produces bursts potentially detectable in these instruments. Published by the American Physical Society 2025

Translation of the downstream ORF from bicistronic mRNAs by human cells: Impact of codon usage and splicing in the upstream ORF.

Biochemistry textbooks describe eukaryotic mRNAs as monocistronic. However, increasing evidence reveals the widespread presence and translation of upstream open reading frames preceding the "main" ORF. DNA and RNA viruses infecting eukaryotes often produce polycistronic mRNAs and viruses have evolved multiple ways of manipulating the host's translation machinery. Here, we introduce an experimental model to study gene expression regulation from virus-like bicistronic mRNAs in human cells. The model consists of a short upstream ORF and a reporter downstream ORF encoding a fluorescent protein. We have engineered synonymous variants of the upstream ORF to explore large parameter space, including codon usage preferences, mRNA folding features, and splicing propensity. We show that human translation machinery can translate the downstream ORF from bicistronic mRNAs, albeit reporter protein levels are thousand times lower than those from the upstream ORF. Furthermore, synonymous recoding of the upstream ORF exclusively during elongation significantly influences its own translation efficiency, reveals cryptic splice signals, and modulates the probability of downstream ORF translation. Our results are consistent with a leaky scanning mechanism facilitating downstream ORF translation from bicistronic mRNAs in human cells, offering new insights into the role of upstream ORFs in translation regulation.

Analysis of nonlinear behavior of viscoelastic damping in musical membranes using physics-informed self-organizing maps

This research offers a new analytical tool that unravels the nonlinear relation between the parameters of Viscoelastic Damping (VD) and the resulting frequency spectrum in musical membranes. Understanding how variations in VD parameters influence the resulting sounds is crucial for developing new tools for artistic expression and for designing musical instruments with distinct sound qualities. In the case of membranophones, the external damping is well understood, while the internal damping due to viscoelastic properties of materials remains unclear. In previous research, VD in musical membranes has been modeled using a Finite-Difference Time-Domain (FDTD) model. Nonetheless, analyzing the complex relationships between the large parameter space of the model and the nonlinear behavior of VD is a challenging task. This study addresses this analysis through physics-based machine learning. We employed a FDTD model of a viscoelastically damped membrane to create a physics-informed dataset, which we subsequently analyzed using Self-Organizing Maps (SOMs). Our findings reveal that the damping coefficient is the primary criterion when clustering the data. Furthermore, we found the internal structure of the cluster to depend on the rate of decay of the memory effect, i.e., the rate at which the energy introduced back into the system decreases. The study also demonstrates the benefits of using principal component analysis for the SOM initialization.

TexSenseGAN: A User-Guided System for Optimizing Texture-Related Vibrotactile Feedback Using Generative Adversarial Network.

Vibration rendering is essential for creating realistic tactile experiences in human-virtual object interactions, such as in video game controllers and VR devices. By dynamically adjusting vibration parameters based on user actions, these systems can convey spatial features and contribute to texture representation. However, generating arbitrary vibrations to replicate real-world material textures is challenging due to the large parameter space. This study proposes a human-in-the-loop vibration generation model based on user preferences. To enable users to easily control the generation of vibration samples with large parameter spaces, we introduced an optimization model based on Differential Subspace Search (DSS) and Generative Adversarial Network (GAN). With DSS, users can employ a one-dimensional slider to easily modify the high-dimensional latent space to ensure that the GAN can generate desired vibrations. We trained the generative model using an open dataset of tactile vibration data and selected five types of vibrations as target samples for the generation experiment. Extensive user experiments were conducted using the generated and real samples. The results indicated that our system could generate distinguishable samples that matched the target characteristics. Moreover, we established a correlation between subjects' ability to distinguish real samples and their ability to distinguish generated samples.

Optimisation of Additively Manufactured Coiled Flow Inverters for Continuous Viral Inactivation Processes

Optimisation of Additively Manufactured Coiled Flow Inverters for Continuous Viral Inactivation Processes

Accelerated Non‐linear Stability Analysis Based on Predictions From Data‐Based Surrogate Models

ABSTRACTIn many applications in computer‐aided engineering, like parametric studies, structural optimization, or virtual material design, a large number of almost similar models must be simulated. Although the individual scenarios may differ only marginally in both space and time, the same amount of effort is invested in each new simulation, without taking into account the experience and knowledge gained in previous simulations. Therefore, we have developed a method that combines data‐based Model Order Reduction (MOR) and reanalysis, exploiting knowledge from previous simulation runs to accelerate computations in multi‐query contexts. While MOR allows reducing model fidelity in space and time without significantly deteriorating accuracy, reanalysis uses results from previous computations as a predictor or preconditioner. In particular, this method enables acceleration of the exact computation of critical points, such as limit and bifurcation points, by the method of extended systems for systems that depend on a set of design parameters, such as material or geometric properties. Such critical points are of utmost engineering significance due to the special characteristics of the structural behavior in their vicinity. Conventional reanalysis methods, like the fold line analysis, can be used to accelerate the computation of critical points of almost similar systems but are limited in their applicability. For the fold line analysis, only small parameter variations are possible as the algorithm may not converge to the correct solution or fail to converge elsewise. Moreover, this method is only suited to finding the first critical points of limit point problems. In contrast to that, our developed data‐based “reduced model reanalysis” method overcomes these problems. Thus, a larger parameter space can be covered. The efficiency of this method is demonstrated for a couple of numerical examples, including standard and isogeometric finite element models.

Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation.

Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational models of neuromodulation can allow the exploration of large parameter spaces and the automatic optimization of stimulation devices and strategies, their high time complexity hinders their use on a large scale. We recently proposed the use of machine learning-based surrogate models to estimate the activation of nerve fibers under electrical stimulation, producing a considerable speed-up with respect to biophysically accurate models of fiber excitation while retaining good predictivity. Here, we characterize the performance of four frequently employed machine learning algorithms and provide an illustrative example of their ability to generalize to unseen stimulation protocols, stimulating sites, and nerve sections. We then discuss how the ability to generalize to such scenarios is relevant to different optimization protocols, paving the way for the automatic optimization of neuromodulation applications.