Deterministic Simulation Research Articles

Hypoxia extreme events in a changing climate: Machine learning methods and deterministic simulations for future scenarios development in the Venice Lagoon

Climate change pressures include the dissolved oxygen decline that in lagoon ecosystems can lead to hypoxia, i.e. low dissolved oxygen concentrations, which have consequences to ecosystem functioning including biogeochemical cycling from mild to severe disruption. The study investigates the potential of machine learning (ML) and deterministic models to predict future hypoxia events. Employing ML models, e.g. Random Forest and AdaBoost, past hypoxia events (2008–2019) in the Venice Lagoon were classified with an F1 score of around 0.83, based on water quality, meteorological, and spatio-temporal factors. Future scenarios (2050, 2100) were estimated by integrating hydrodynamic-biogeochemical and climate projections. Results suggest hypoxia events will increase from 3.5 % to 8.8 % by 2100, particularly in landward lagoon areas. Summer prediction foresee a rise from 118 events to 265 by 2100, with a longer hypoxia-prone season. This model is a valuable tool for developing hypoxia scenarios, aiding in identifying restoration hotspots for climate-threatened lagoons.

Efficient Approximation of Varying Fiber Orientation States in Injection Molded Parts Under Consideration of Multiple Manufacturing Uncertainties

AbstractThe production of high-quality fiber reinforced polymer parts is an important aspect in several industrial areas. However, due to unavoidable uncertainties in material and manufacturing processes, the part quality scatters. One important aspect here is the fiber orientation, being crucial for the thermo-mechanical properties of the part and being influenced by the uncertain material state and process conditions. Process simulations are an important tool for predicting the fiber orientation, but state-of-the-art simulations are normally deterministic and represent only one specific case. Performing enough deterministic simulations to model manufacturing uncertainties requires high numerical effort. Therefore, this work presents methods to quickly and efficiently approximate the fiber orientation under varying material and process parameters, requiring only a few simulations as input. Different schemes for approximation are evaluated and compared with each other and with 3D process simulations.

Stochastic Analysis of Epithelial/Absorbed Power Density in Multilayered Planar Skin Model With Uncertain Tissue Electric Properties

AbstractStochastic analysis of the absorbed power density (APD) on skin surface exposed to radiation of halfwave dipole at 10, 30 and 90 GHz and for different antenna‐body distances is presented. Skin tissue is modeled as a half‐space consisting of one layer (skin) or three layers (skin, fat and muscle) whose permittivities and conductivities are uncertain. Deterministic part is based on numerical solution of Pocklington equation via Galerkin Bubnov Indirect Boundary Element method (GB‐IBEM) and numerical integration of the corresponding field integrals. Uncertainty from tissue electric parameters is propagated to APD via non‐intrusive Stochastic Collocation method (SCM) in order to compute stochastic moments of APD. For 1‐layered model APD stochastic moments are computed with 3 deterministic simulations. On the other hand APD mean and variance for 3 layered model are successfully computed with 13 deterministic simulations while skewness and kurtosis require 85 deterministic simulations. The ratio of APD standard deviation and mean decreases with frequency thus indicating that the uncertainty in the tissue electric properties has smaller effect on APD uncertainty at higher frequencies. Finally, sensitivity analysis carried out for both 1‐layered and 3‐layered models indicates the same conclusions. At 10 GHz skin permittivity and conductivity are the two most important parameters. However, as frequency increases the impact of skin conductivity prevails. This indicates that in frequency range 10–90 GHz the APD uncertainty quantification and sensitivity analysis can be carried out by using only 2‐dimensional stochastic model.

Characterisation and simulated evaluation of electricity management strategies for Prosumers, Prosumagers, and other end-users

ABSTRACT Distributed photovoltaic generation has been raised as a dependable electricity substitute for implementing a decarbonised energy transition. The dynamics of PV systems with battery storage have led to new electricity end-user classifications that select their electricity systems and resources wisely, i.e. Prosumers and Prosumagers. This work presents a technical and economic assessment to analyse the effect of decision-making independent end-users and electricity management strategies through a novel time-discrete deterministic simulation model. The end-user's choice of battery storage technology and their approach to electricity management emerge as pivotal factors influencing their strategic decisions. Adopting a prosumer strategy, such as storing and selling surplus self-production, reduces the Levelized Cost of Energy (LCOE), benefiting the end-user economically. Notably, the most effective battery storage technology may only sometimes be the one that is most readily available in the market. These findings enhance our understanding of novel end-user types and underscore the practical implications of their evolutionary shift towards independence.

Uncertainty Propagation of Monte Carlo Cross-Section Generation for Graphite-Moderated Pebble Bed Reactors

Monte Carlo codes have become increasingly popular for generating homogenized few-group cross-section data, especially for advanced reactor designs that have complex geometries and nontraditional compositions. However, the stochastic nature of Monte Carlo processes has the potential to introduce additional statistical uncertainties in the overall uncertainty in the prediction of core behavior. The work performed in this research quantified the additional uncertainty introduced by the use of Monte Carlo multigroup cross sections into the analysis of graphite-moderated pebble bed reactors. In this research, the objective was achieved by performing uncertainty quantification for the key output parameters in deterministic steady-state and transient safety calculations. The results show that when the homogenized multigroup cross sections are generated with a sufficient number of neutron histories in the Monte Carlo calculation, the uncertainties in the subsequent deterministic simulations caused by the Monte Carlo cross-section uncertainty are negligible compared to the contributions from the uncertainties of other input parameters.

Physiological control for left ventricular assist devices based on deep reinforcement learning.

The improvement of controllers of left ventricular assist device (LVAD) technology supporting heart failure (HF) patients has enormous impact, given the high prevalence and mortality of HF in the population. The use of reinforcement learning for control applications in LVAD remains minimally explored. This work introduces a preload-based deep reinforcement learning control for LVAD based on the proximal policy optimization algorithm. The deep reinforcement learning control is built upon data derived from a deterministic high-fidelity cardiorespiratory simulator exposed to variations of total blood volume, heart rate, systemic vascular resistance, pulmonary vascular resistance, right ventricular end-systolic elastance, and left ventricular end-systolic elastance, to replicate realistic inter- and intra-patient variability of patients with a severe HF supported by LVAD. The deep reinforcement learning control obtained in this work is trained to avoid ventricular suction and allow aortic valve opening by using left ventricular pressure signals: end-diastolic pressure, maximum pressure in the left ventricle (LV), and maximum pressure in the aorta. The results show controller obtained in this work, compared to the constant speed LVAD alternative, assures a more stable end-diastolic volume (EDV), with a standard deviation of 5 mL and 9 mL, respectively, and a higher degree of aortic flow, with an average flow of 1.1 L/min and 0.9 L/min, respectively. This work implements a deep reinforcement learning controller in a high-fidelity cardiorespiratory simulator, resulting in increases of flow through the aortic valve and increases of EDV stability, when compared to a constant speed LVAD strategy.

Pollution risk evaluation of groundwater wells based on stochastic and deterministic simulation of aquifer lithology

Groundwater pollution risk evaluation is an important basis for developing groundwater protection measures and management strategies, and its accuracy directly affects the effectiveness of protection measures. The heterogeneity of the aquifer significantly affects the transport process of pollutants, increasing the uncertainty of pollutant risk assessment. However, in the actual site, borehole data that reveal aquifer heterogeneity are costly, and only a limited number of borehole data are available, which cannot accurately describe the heterogeneity of the aquifer, thus limiting the accuracy of groundwater pollution risk assessment. In order to overcome the above problems, this paper proposes a groundwater pollution risk assessment framework based on the stochastic and deterministic simulation of aquifer lithology. Based on the statistical characteristics of the change of lithology type in the actual borehole, the framework uses Markov chain to generate some sets of random lithology field and transforms them into heterogeneity parameter field, so as to realize the stochastic assessment of the pollution risk of groundwater resource wells. Furthermore, combined with the pumping test data, the parameter field that is most suitable for the actual situation is selected to evaluate the pollution risk deterministically. Finally, the stochastic and deterministic results are combined to comprehensively evaluate the pollution risk of groundwater resource wells. Through a case study in a river valley plain, the feasibility of the above framework is verified, and good application effects are achieved. This study provides a feasible method for accurately assessing groundwater pollution risk, which is helpful to reduce the impact of uncertain factors on pollution risk assessment, and thus provides a more reliable basis for groundwater management and decision-making.

Valence-dependent dopaminergic modulation during reversal learning in Parkinson’s disease: A neurocomputational approach

Reinforcement learning, crucial for behavior in dynamic environments, is driven by rewards and punishments, modulated by dopamine (DA) changes. This study explores the dopaminergic system’s influence on learning, particularly in Parkinson’s disease (PD), where medication leads to impaired adaptability. Highlighting the role of tonic DA in signaling the valence of actions, this research investigates how DA affects response vigor and decision-making in PD. DA not only influences reward and punishment learning but also indicates the cognitive effort level and risk propensity in actions, which are essential for understanding and managing PD symptoms.In this work, we adapt our existing neurocomputational model of basal ganglia (BG) to simulate two reversal learning tasks proposed by Cools et al. We first optimized a Hebb rule for both probabilistic and deterministic reversal learning, conducted a sensitivity analysis (SA) on parameters related to DA effect, and compared performances between three groups: PD-ON, PD-OFF, and control subjects.In our deterministic task simulation, we explored switch error rates after unexpected task switches and found a U-shaped relationship between tonic DA levels and switch error frequency. Through SA, we classify these three groups. Then, assuming that the valence of the stimulus affects the tonic levels of DA, we were able to reproduce the results by Cools et al.As for the probabilistic task simulation, our results are in line with clinical data, showing similar trends with PD-ON, characterized by higher tonic DA levels that are correlated with increased difficulty in both acquisition and reversal tasks.Our study proposes a new hypothesis: valence, signaled by tonic DA levels, influences learning in PD, confirming the uncorrelation between phasic and tonic DA changes. This hypothesis challenges existing paradigms and opens new avenues for understanding cognitive processes in PD, particularly in reversal learning tasks.

Evaluation of surface texturing on chrome-coated cylinder liners via deterministic mixed lubrication simulation

This study investigates the mixed lubrication performance of various surface texture configurations in the piston ring/cylinder liner conjunction of a two-stroke internal combustion engine using a deterministic mixed lubrication model. The numerical model simultaneously solves the Reynolds equation with mass-conserving cavitation to calculate inter-asperity hydrodynamic pressures and an elastic, perfectly plastic, rough contact model to determine contact pressures at each asperity interaction. Gaussian Mixture Model clustering was employed to enhance surface characterization. The deterministic simulation approach considers the full-scale representation of the cylinder liner topography to accurately capture the influence of surface features on the hydrodynamic support and friction under mixed lubrication conditions. The investigated cylinder liners were initially hard-chrome-coated and honed, resulting in a stochastic arrangement of surface pores, and then deterministic patterns of surface pockets were created by micro electrodischarge machining (EDM). Surface measurements were performed using laser interferometry, providing input for the mixed lubrication simulations. The study also explored the virtual removal of ridges formed around the pockets by the EDM technique. Key findings indicate that the stochastic texture outperformed the hybrid texture (stochastic + deterministic) in the boundary and mixed lubrication regimes, showing higher hydrodynamic support at low separations but increased hydrodynamic shear stresses at higher speeds. Conversely, deterministic textures exhibited a significant decrease in average hydrodynamic shear stress at high velocities. These results highlight the critical role of surface texture in tribological behavior and suggest that localized textures on cylinder liners can potentially optimize engine performance. The study recommends further exploration of a broader range of texture geometries, densities, and distribution patterns to enhance engine design strategies.

Numerical simulations of equilibrium, kinetics, and chain length distribution in linear polycondensation proceeding in nano-scale small volumes: An apparent violation of the law of mass action

A simple polycondensation process Mi + Mj ⇄ Mi+j + Z, proceeding in dispersion of nano-scale droplets was described by analytical solutions and differential equations giving insight into equilibrium and kinetics of the process. Stochastic and deterministic simulations show that the statistical nature of polycondensation, both irreversible and reversible one, affects the way the macromolecules of different lengths are formed and distributed among droplets. The droplet distributions in respect to the number of reacting chains and the chain length distributions depend, for the given conversion, on the polycondensation equilibrium constant Kcond and the initial conditions: monomer concentration and the number of its molecules in a droplet. The apparent equilibrium constants of condensation K¯ij=[Mi+j]¯e[Z]¯e/[Mi]¯e[Mj]¯e depend on oligomer/polymer sizes as well as on the initial number of monomer molecules in a droplet. This apparent violation of the law of mass action was explained by stochasticity of involved processes. Moreover, when the polycondensation byproduct Z is being removed reversibly from the system, K¯ij depends also on average equilibrium concentration [Z]¯e. The indicated effects are observed not only for ideally dispersed systems (all droplets contain initially the same number of monomer molecules), but also when the initial numbers of monomer molecules conform the Poisson distribution. However, the chain length distribution as well as kinetics and K¯ij differ here from those observed for systems with uniform distribution.

Remote sensing of high energy charged particle current (HEC) for megavoltage therapeutic electron beams

Objective. We demonstrate detection of high energy particle current (HEC) for MeV therapeutic electron beams. Detection of HEC comprises of remote sensing or acquiring information about HEC inside radiation transport medium from a distance outside of the medium. Approach. HEC is self-propelled motion of charged particles through a radiation transport medium. Remote sensing of HEC is embodied in an experimental setup, which includes homogeneous and heterogeneous phantoms irradiated with 4–15 MeV electron beams and two large area parallel-plane electrodes extraneous to the phantoms providing two-parameter detection. We also introduce a new type of scanning method (depth-scan) for probing object properties along the beamline axis. Main Results. Deterministic radiation transport simulations and measurements agree, considering differences in simulation vs experimental geometry and experimental uncertainties. Significance. This method may be suitable for range detection of charged particle beams, or for probing of radiation opaque objects in non-destructive testing.

Parameter identification of vibratory conveying systems including statistical part behavior

This work presents a complete workflow for the parameter identification of an MBS model representing a vibratory conveying system. First, the MBS model built within the multibody dynamics tool HOTINT is shown. Essential components, such as contact modeling and adaptive time step control, are explained in detail. The model includes a considerable amount of parameters that need to be identified. Altough, this model is fully deterministic, the complex contact dynamics can cause major effect on results by minor parameter variations. Thus, parameter variations usually show statistical variance, which is characteristic for real conveying processes too. For parameter identification it is important to detect parameters that significantly correlate with results. A method that allows to distinguish significant and non-significant parameters is presented. The application to the multibody system shows that stiffness, restitution and sliding friction of the contact model are the essential parameters. Next, the parameter identification is discussed with particular attention to multimodal distributions, that can occur in conveying systems. Furthermore, the integration of statistics into the deterministic simulation model is discussed. In order to demonstrate the applicability of the proposed methods, a parameter identification is performed based on measurement data of a single part on a real conveying system in the development site of STIWA Automation GmbH. Finally, the validity of the fully parametrized simulation model is shown by investigating geometric adjustments of the conveyor. In order to further verify the identified set of parameters, a conveying process with multiple parts is analyzed and compared in measurement and simulation.

Food chain without giants: modelling the trophic impact of bowhead whaling on little auk populations in the Atlantic Arctic.

In the Atlantic Arctic, bowhead whales (Balaena mysticetus) were nearly exterminated by European whalers between the seventeenth and nineteenth centuries. The collapse of the East Greenland-Svalbard-Barents Sea population, from an estimated 50 000 to a few hundred individuals, drastically reduced predation on mesozooplankton. Here, we tested the hypothesis that this event strongly favoured the demography of the little auk (Alle alle), a zooplanktivorous feeder competitor of bowhead whales and the most abundant seabird in the Arctic. To estimate the effect of bowhead whaling on little auk abundance, we modelled the trophic niche overlap between the two species using deterministic simulations of mesozooplankton spatial distribution. We estimated that bowhead whaling could have led to a 70% increase in northeast Atlantic Arctic little auk populations, from 2.8 to 4.8 million breeding pairs. While corresponding to a major population increase, this is far less than predicted by previous studies. Our study illustrates how a trophic shift can result from the near extirpation of a marine megafauna species, and the methodological framework we developed opens up new opportunities for marine trophic modelling.

Uncertainty Quantification in SAR Induced by Ultra-High-Field MRI RF Coil via High-Dimensional Model Representation.

As magnetic field strength in Magnetic Resonance Imaging (MRI) technology increases, maintaining the specific absorption rate (SAR) within safe limits across human head tissues becomes challenging due to the formation of standing waves at a shortened wavelength. Compounding this challenge is the uncertainty in the dielectric properties of head tissues, which notably affects the SAR induced by the radiofrequency (RF) coils in an ultra-high-field (UHF) MRI system. To this end, this study introduces a computational framework to quantify the impacts of uncertainties in head tissues' dielectric properties on the induced SAR. The framework employs a surrogate model-assisted Monte Carlo (MC) technique, efficiently generating surrogate models of MRI observables (electric fields and SAR) and utilizing them to compute SAR statistics. Particularly, the framework leverages a high-dimensional model representation technique, which constructs the surrogate models of the MRI observables via univariate and bivariate component functions, approximated through generalized polynomial chaos expansions. The numerical results demonstrate the efficiency of the proposed technique, requiring significantly fewer deterministic simulations compared with traditional MC methods and other surrogate model-assisted MC techniques utilizing machine learning algorithms, all while maintaining high accuracy in SAR statistics. Specifically, the proposed framework constructs surrogate models of a local SAR with an average relative error of 0.28% using 289 simulations, outperforming the machine learning-based surrogate modeling techniques considered in this study. Furthermore, the SAR statistics obtained by the proposed framework reveal fluctuations of up to 30% in SAR values within specific head regions. These findings highlight the critical importance of considering dielectric property uncertainties to ensure MRI safety, particularly in 7 T MRI systems.

Effect of Hepatic Impairment on Trofinetide Exposures Using an In Silico Physiologically Based Pharmacokinetic Model.

Trofinetide is the first drug to be approved for the treatment of Rett syndrome. Hepatic impairment is not expected to affect the pharmacokinetic (PK) profile of trofinetide because of predominant renal excretion. This study was conducted to help understand the potential impact of any hepatic impairment on trofinetide PK. This study used physiologically based PK modeling to estimate trofinetide exposure (maximum drug concentration and area under the concentration-time curve from time zero to infinity) in virtual patients with mild, moderate, and severe hepatic impairment (per Child-Pugh classification) compared with virtual healthy subjects following a 12 g oral trofinetide dose. In individual deterministic simulations for matched individuals and stochastic simulations at the population level (100 virtual individuals simulated per population), as anticipated, predicted plasma exposures were similar for healthy subjects and for patients with mild, moderate, and severe hepatic impairment. However, predicted blood concentration exposures slightly increased with increasing severity of hepatic impairment because of change in hematocrit levels. This study indicates that hepatic impairment is not expected to have a clinically relevant effect on exposure to trofinetide.

Analysis of Carbon Emission Reduction with Using Low-Carbon Demand Response: Case Study of North China Power Grid

The power sector is the single industry with the largest carbon emission in China, the carbon emission of which accounts for more than 40% of China’s total carbon emissions. In relevant research on the simulation of power system operation, current studies focus more on energy conservation and economical operation, while few consider the low-carbon optimization of the power system from the perspective of carbon emissions. In addition, in relevant research on carbon reduction in the power system, current studies focus more on controlling the direct carbon emission of the source side and less on the indirect carbon emissions of the load side, which focus on the reverse effect of a user’s electricity consumption behavior on the carbon reduction goals of the power system. This article delved into a deterministic simulation model of power system operation based on time series load curves and proposed a carbon reduction mechanism called the low-carbon demand response mechanism, which guides users to actively respond and reduce the carbon emission of power systems. In addition, this article conducted an empirical analysis based on the planning data of the North China Power Grid. To minimize carbon emissions, a simulation of low-carbon optimization operation for the North China Power Grid in 2040 was carried out. Then, based on the simulation results, an analysis of the carbon reduction benefits of low-carbon demand response was carried out. Ultimately, the empirical analysis verified that low-carbon optimization operation and low-carbon demand response technology possess significant carbon reduction potential for the power system.

Response to Selection of Indigenous Chicken in Rwanda Using Within-Breed Selection Strategy

The study evaluated response to selection from within-breed selection strategy for conventional (<I>CBS</I>) and genomic (<I>GBS</I>) breeding schemes. These breeding schemes were evaluated in both high-health environments (nucleus) and smallholder farms (commercial). Breeding goal was to develop a dual-purpose IC for both eggs and meat through selective breeding. Breeding objectives were body weight (BW), egg weight (EW), egg number (EN) and resistance to Newcastle disease (AbR). A deterministic simulation was performed to evaluate rates of genetic gain and inbreeding. Base population in the nucleus was made up of 40 cockerels and 200 pullets. Selection pressure was 4% and 20% in the males and the females, respectively. The impact of nucleus size and selection pressure on rates of genetic gain and inbreeding of the breeding program was investigated through sensitivity analysis. SelAction software was used to predict rates of genetic gain and inbreeding. Results showed that using <I>CBS</I> in the nucleus, the breeding goal was 340.41\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\$ and 1.13 times higher than that in the commercial flock. Inbreeding rate per generation of selected chicken in the nucleus was 1.45% and lower by 1.32 times compared to their offspring under smallholder farms. Genetic gains per generation in the nucleus for BW and EN traits were 141.10 g and 1.07 eggs and 1.12 and 1.10 times greater than those in smallholder farms, respectively. With <I>GBS</I>, the breeding goal was increased by 3.00 times whereas inbreeding rate was reduced by 3.15 times. Besides, using <I>GBS</I>, the selected birds in the nucleus were relatively similar to those in a commercial environment. Finally, the study revealed that the nucleus size and mating ratio influence the rates of genetic gain and inbreeding in both <I>GBS</I> and <I>CBS.</I> This study shows that IC in Rwanda have the potential to be improved through within-breed selection strategy using either <I>CBS</I> or <I>GBS</I>.

Assessing the influence of the algebraic structure of q-ary lattices on the complexity of cryptanalysis of problems on lattices

The work is devoted to the influence of the algebraic structure of q-ary lattices on the complexity of cryptanalysis of cryptographic problems on lattices. The root-mean-square errors for existing lattice reduction models are obtained. It is shown that the deterministic Albrecht-Lee simulator shows the smallest mean square deviations on small lattices. Collapsibility estimates for nesting and decoding attacks on the algebraic structure of q-ary lattices have been found. A new method for selecting attack parameters has been proposed for the decoding attack. It is shown that a decoding attack with such a choice of parameters can be at the expense of nesting attacks. To distribute the power of the secret vector in decoding attacks, which differ from the normal one, the approximation method is used by the normal division, which minimizes the Kolmogorov-Smirnov equation and calculates the optimal values for some distributions. There are no parameters for any divisions. For the SIS problem, a new approach to safety assessment has been proposed, which takes into account the possibility of various Euclidean norms. Based on the new approach, estimates of the SIS problem for Crystals-Dilithium electronic signature schemes have been calculated.

A Dynamic Material Flow Model for Risk-Informed Decision Making in Decarbonizing Global Aluminum Manufacturing

Abstract Aluminum is the world's second most consumed metal, and its production contributes substantially to global greenhouse gas (GHG) emissions. When formulating decarbonization strategies, it is imperative to ensure their coherence and alignment with existing industrial practices and standards. A material flow analysis (MFA) is needed to gain a holistic and quantitative understanding of the flows and stocks of products/materials associated with all participants within the supply chain. To support risk-informed decision policymaking in decarbonizing aluminum manufacturing, this study develops a dynamic system model that maps global aluminum flows and computes their embedded GHG emissions. A baseline scenario is devised to reflect the current business and operation landscape, and three decarbonization strategies are proposed. Deterministic simulation is performed to generate dynamic material flows and performance metrics. Monte Carlo simulation is then implemented to evaluate the robustness of the system's performance under demand uncertainties. The results reveal the immense carbon implications of material efficiency, as well as the preponderant role of post-consumer scrap recycling in decarbonizing aluminum manufacturing. Informed by simulation outputs, macro decarbonization guidelines are formulated for various criteria. The object-oriented programming framework that underlies the dynamic MFA may be integrated with network analysis, agent-based simulation, and geospatial interfaces, which may lay the foundation for modeling more fine-grained material flows and supply chain structures.

Efficient and accurate uncertainty quantification in engineering simulations using time-separated stochastic mechanics

A robust method for uncertainty quantification is undeniably leading to a greater certainty in simulation results and more sustainable designs. The inherent uncertainties of the world around us render everything stochastic, from material parameters, over geometries, up to forces. Consequently, the results of engineering simulations should reflect this randomness. Many methods have been developed for uncertainty quantification for linear elastic material behavior. However, real-life structure often exhibit inelastic material behavior such as visco-plasticity. Inelastic material behavior is described by additional internal variables with accompanying differential equations. This increases the complexity for the computation of stochastic quantities, e.g., expectation and standard deviation, drastically. The time-separated stochastic mechanics is a novel method for the uncertainty quantification of inelastic materials. It is based on a separation of all fields into a sum of products of time-dependent but deterministic and stochastic but time-independent terms. Only a low number of deterministic finite element simulations are then required to track the effect of (in)homogeneous material fluctuations on stress and internal variables. Despite the low computational effort the results are often indistinguishable from reference Monte Carlo simulations for a variety of boundary conditions and loading scenarios.