Unknown Input Parameters Research Articles

INVESTIGATIONS ON THERMAL PERFORMANCE OF AN ELECTRONIC BOARD USING CONDUCTION-BASED FINITE ELEMENT METHOD WITH A NEW MODELING APPROACH

Electronic systems are used in almost all areas of industry, with an increasing power consumption rate. This trend makes thermal management of electronics compulsory in order for proper operation. Several methods can be employed to examine electronics’ thermal behavior. Conduction-based Finite Element Method (FEM) for heat transfer analysis is one of them; providing accurate solutions within short solution times is one of its outstanding advantages. Nevertheless, the fluid inside or around the system, usually air for electronic systems, is not included directly in the conduction-based FEM analysis model. This is an essential deficiency in terms of solution accuracy. If this drawback is overcome, conduction-based FEM will become a preferred analysis method, especially for transient problems under natural convection. In this study, a conduction-based FEM analysis model of an electronic board with two heat-dissipating components inside an enclosure under transient natural convection was developed. The procedure of the model involves the correction of unknown input parameters. An experimental investigation was performed, the results of which were used as reference values for the correction process. These unknown parameters were determined iteratively. The iteration was continued until the results of the analysis and those of the experiment matched. The difference between the results of the analysis and those of the experiment was less than 2-3°C. Some parametrical thermal investigations were performed on the electronic board using the final analysis model.

Approximate Bayesian Computation for structural identification of ancient tie-rods using noisy modal data

Masonry arches and vaults are common historic structural elements that frequently experience asymmetric loading due to seismic action or abutment settlements. Over the past few decades, numerous studies have sought to enhance our understanding of the structural behavior of these elements for the purpose of preventive conservation. The assessment of the structural performance of existing constructions typically relies on effective numerical models guided by a set of unknown input parameters, including geometry, mechanical characteristics, physical properties, and boundary conditions. These parameters can be estimated through deterministic optimization functions aimed at minimizing the discrepancy between the output of a numerical model and the measured dynamic and/or static structural response. However, deterministic approaches overlook uncertainties associated with both input parameters and measurements. In this context, the Bayesian approach proves valuable for estimating unknown numerical model parameters and their associated uncertainties (posterior distributions). This involves updating prior knowledge of model parameters (prior distributions) based on current measurements and explicitly considering all sources of uncertainties affecting observed quantities through likelihood functions. However, two significant challenges arise: the likelihood function may be unknown or too complex to evaluate, and the computational costs for approximating the posterior distribution can be prohibitive. This study addresses these challenges by employing Approximate Bayesian Computation (ABC) to handle the intractable likelihood function. Additionally, the computational burden is mitigated through the use of accurate surrogate models such as Polynomial Chaos Expansions (PCE) and Artificial Neural Networks (ANN). The research focuses on setting up numerical models for simple structural systems (tie-rods) and inferring unknown input parameters, such as mechanical properties and boundary conditions, through Bayesian updating based on observed structural responses (modal data, strains, displacements). The main novelties of this research regard, on the one hand, the proposal of a methodology for obtaining a reliable estimate of the axial force in ancient tie-rods accounting for different sources of uncertainty and, on the other hand, the application of ABC to obtain the structural identification inverse problem solution.

Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making

Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.

Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm

The aim of this study is to explore the characteristics of an active Free-Piston Stirling Engine (AFPSE) through the use of machine learning methods. Due to the time-intensive nature of extracting simulation results from complex thermal equations, an Artificial Neural Network (ANN) is utilized to expedite the process. To construct a nonlinear model, 5000 samples are extracted from simulation results. Input parameters included in the model are the hot and cold source temperatures, the voltage given to the DC motor, spring stiffness, and the mass of the power piston, while output parameters are the amplitude and frequency of power piston displacement. The proposed ANN model structure comprises two hidden layer with 10 and 20 neurons, respectively, indicating the applicability of the ANN model in estimating significant parameters of AFPSE in a shorter amount of time. The firefly optimization algorithm is utilized to determine the unknown input parameters of ANN and maximize the output power. Results indicate that a maximum output power of 23.07 W can be attained by applying 8.5 V voltage on the DC motor. This study highlights the potential of machine learning techniques to explore the primary features of AFPSE.

Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters

Ensuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment. Currently, the measurement of frictional contact temperature remains a formidable challenge, while the accuracy of simulation results from conventional numerical methods remains uncertain. In this study, a PINN model that incorporates physical information, such as partial differential equation (PDE) and boundary conditions, into neural networks is proposed to solve forward and inverse problems of frictional contact temperature. Compared to the traditional numerical calculation method, the preprocessing of the PINN is more convenient. Another noteworthy characteristic of the PINN is that it can combine data to obtain a more accurate temperature field and solve inverse problems to identify some unknown parameters. The experimental results substantiate that the PINN effectively resolves the forward problems of frictional contact temperature when provided with known input conditions. Additionally, the PINN demonstrates its ability to accurately predict the friction temperature field with an unknown input parameter, which is achieved by incorporating a limited quantity of easily measurable actual temperature data. The PINN can also be employed for the inverse identification of unknown parameters. Finally, the PINN exhibits potential in solving inverse problems associated with frictional contact temperature, even when multiple input parameters are unknown.

Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks.

Machine learning (ML)-based emulators improve the calibration of decision-analytical models, but their performance in complex microsimulation models is yet to be determined. We demonstrated the use of an ML-based emulator with the Colorectal Cancer (CRC)-Adenoma Incidence and Mortality (CRC-AIM) model, which includes 23 unknown natural history input parameters to replicate the CRC epidemiology in the United States. We first generated 15,000 input combinations and ran the CRC-AIM model to evaluate CRC incidence, adenoma size distribution, and the percentage of small adenoma detected by colonoscopy. We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants (i.e., XGBoost, LightGBM, CatBoost) and compared their performance. We evaluated 10 million potential input combinations using the selected emulator and examined input combinations that best estimated observed calibration targets. Furthermore, we cross-validated outcomes generated by the CRC-AIM model with those made by CISNET models. The calibrated CRC-AIM model was externally validated using the United Kingdom Flexible Sigmoidoscopy Screening Trial (UKFSST). The DNN with proper preprocessing outperformed other tested ML algorithms and successfully predicted all 8 outcomes for different input combinations. It took 473 s for the trained DNN to predict outcomes for 10 million inputs, which would have required 190 CPU-years without our DNN. The overall calibration process took 104 CPU-days, which included building the data set, training, selecting, and hyperparameter tuning of the ML algorithms. While 7 input combinations had acceptable fit to the targets, a combination that best fits all outcomes was selected as the best vector. Almost all of the predictions made by the best vector laid within those from the CISNET models, demonstrating CRC-AIM's cross-model validity. Similarly, CRC-AIM accurately predicted the hazard ratios of CRC incidence and mortality as reported by UKFSST, demonstrating its external validity. Examination of the impact of calibration targets suggested that the selection of the calibration target had a substantial impact on model outcomes in terms of life-year gains with screening. Emulators such as a DNN that is meticulously selected and trained can substantially reduce the computational burden of calibrating complex microsimulation models. Calibrating a microsimulation model, a process to find unobservable parameters so that the model fits observed data, is computationally complex.We used a deep neural network model, a popular machine learning algorithm, to calibrate the Colorectal Cancer Adenoma Incidence and Mortality (CRC-AIM) model.We demonstrated that our approach provides an efficient and accurate method to significantly speed up calibration in microsimulation models.The calibration process successfully provided cross-model validation of CRC-AIM against 3 established CISNET models and also externally validated against a randomized controlled trial.

Utilizing Data-Driven Optimization to Automate the Parametrization of Kinetic Monte Carlo Models.

Kinetic Monte Carlo (kMC) simulations are a popular tool to investigate the dynamic behavior of stochastic systems. However, one major limitation is their relatively high computational costs. In the last three decades, significant effort has been put into developing methodologies to make kMC more efficient, resulting in an enhanced runtime efficiency. Nevertheless, kMC models remain computationally expensive. This is in particular an issue in complex systems with several unknown input parameters where often most of the simulation time is required for finding a suitable parametrization. A potential route for automating the parametrization of kinetic Monte Carlo models arises from coupling kMC with a data-driven approach. In this work, we equip kinetic Monte Carlo simulations with a feedback loop consisting of Gaussian Processes (GPs) and Bayesian optimization (BO) to enable a systematic and data-efficient input parametrization. We utilize the results from fast-converging kMC simulations to construct a database for training a cheap-to-evaluate surrogate model based on Gaussian processes. Combining the surrogate model with a system-specific acquisition function enables us to apply Bayesian optimization for the guided prediction of suitable input parameters. Thus, the amount of trial simulation runs can be considerably reduced facilitating an efficient utilization of arbitrary kMC models. We showcase the effectiveness of our methodology for a physical process of growing industrial relevance: the space-charge layer formation in solid-state electrolytes as it occurs in all-solid-state batteries. Our data-driven approach requires only 1-2 iterations to reconstruct the input parameters from different baseline simulations within the training data set. Moreover, we show that the methodology is even capable of accurately extrapolating into regions outside the training data set which are computationally expensive for direct kMC simulation. Concluding, we demonstrate the high accuracy of the underlying surrogate model via a full parameter space investigation eventually making the original kMC simulation obsolete.

External drivers of BOLD signal's non-stationarity.

A fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. This dynamical systems model of the brain offers information on the structure and dimensionality of the BOLD signal’s external drivers and shines a light on the likely external sources contributing to the BOLD signal’s non-stationarity. Our findings show the role of exogenous inputs in the BOLD dynamics and highlight the importance of accounting for external inputs to unravel the brain’s time-varying functional dynamics.

МЕТОД РАСЧЕТА СКОРОСТИ ЗВУКА В МОРСКОЙ ВОДЕ ДЛЯ ИЗМЕРЕНИЯ БЫСТРОМЕНЯЮЩИХСЯ ОКЕАНИЧЕСКИХ ПРОЦЕССОВ

The paper presents two compact polynomial equations expressing the dependence of the sound speed of sea water on the input parameters: in situ temperature, hydrostatic pressure and absolute salinity. The equations are obtained by approximating the standard International Thermodynamic Equation of Seawater-2010 (TEOS-10). The first equation is intended to be used for the technical purposes and reproduces the TEOS-10 data on the seawater sound speed over the wide range water environment parameters with a standard deviation of 0.987 cm/s. The second, more accurate equation is for the scientific use and reproduces the TEOS-10 data on the seawater sound speed over the oceanographic range water environment parameters with a standard deviation of 0.113 cm/s. The equations can be used to solve direct and inverse computational problems. In the direct computational problem, the sound speed value is calculated from the three input parameters measured values. In the inverse computational problem, the value of an unknown input parameter is determined from the measured values of the sound speed and two other input parameters. As a result of the experiments, the higher efficiency of the developed equations was confirmed in terms of their speed, saving energy and computer memory in comparison with the use of the International Equation TEOS-10. The developed equations, algorithms and programs for microcontrollers are convenient tools to equip various offshore platforms and especially fastmoving submersibles. The application of these equations makes it possible to obtain experimental data with high accuracy, to carry out measurements in real time in a rapidly changing aquatic environment, which can also be used to correct the movement of the submersibles.

Variational data assimilation to improve subsurface drainage model parameters

• Identification of the most influential model parameters using the global adjoint SA. • Variational data assimilation significantly reduces the water volume discrepancy. • The gradient-based stochastic procedure performs without the need for the prior. • A new hybrid BV method offers an automatic tool to select a robust parameter set. Variational data assimilation (VDA) has been implemented to enhance the estimation of the unknown input parameters of a new agricultural subsurface drainage model (SIDRA-RU) through assimilating drainage discharge observations. The adjoint model of SIDRA-RU has been successfully generated through the generic automatic differentiation tool (TAPENADE). First, the adjoint model is used to explore the local and global adjoint sensitivities of the valuable function defined over the drainage discharge simulations with respect to model input parameters. Next, the most influential parameters are estimated by applying the Variational DA approach. The performed sensitivity analysis shows that the most influential parameters on drainage discharge are those controlling the dynamics of the water table; the second most influential parameters manage the drainflow start of each drainage season. Compared to an alternative gradient-free calibration performance, the estimation of these governing parameters by the variational method improves the overall quality of the drainage discharge prediction, in particular in terms of the cumulative water volume. Improved parameters generate less than 5 mm (1%) of the discrepancy between simulated and observed water volumes, based on the five years of daily discharge observations on the Chantemerle agricultural parcels (36 ha). Preliminary numerical tests have shown the potential presence of multiple local minima, thus pointing out the equifinality issues. The latter can be highlighted by the self-compensation of both the physical soil parameters and the main conceptual parameters. For improving the robustness of the parameter estimates, a novel hybrid “Bayesian Variational” method is suggested. This method is based on the Bayesian averaging of an ensemble of optimal estimates.

Bayesian inversion of laboratory experiments of transport through limestone fractures

In this study, a novel experimental setup is proposed for which a column filled with glass beads and parallelepiped-shaped limestone beams is used to reconstruct a multiple fracture limestone media. The proposed setup produces asymmetric breakthrough curves (BTCs) that are consistent with the shape expected from the past field and lab-scale studies. Three transport experiments have been conducted under fast, medium, and slow flow velocity conditions.The research focuses on parameter and state estimation using Bayesian inference via Markov Chain Monte Carlo (MCMC) sampler, investigating the degree to which three models of transport through fractured media can reproduce the experimental results under the three flow conditions. The first transport model, named ADE, is based on the equivalent porous medium (EPM) approach and corresponds to the linear advection dispersion equation (ADE). The second model, named FOMIM (first-order mobile immobile), is based on the mobile/immobile approach and uses the dual porosity model with a linear first-order transfer between mobile and immobile regions. The third model, named NLMIM (non-linear mobile-immobile), uses a nonlinear transfer function between these two regions.The results of the three models show that almost all the unknown model input parameters can be well-estimated with narrow confidence intervals using the MCMC method. With respect to state estimation, the ADE model fails to reproduce correctly the tail of the BTCs observed under slow and medium flow conditions. The FOMIM model improves the tailing of the BTCs, but significant discrepancies remain between simulated and measured concentrations. The NLMIM model with velocity-dependent parameters is the only model that captures BTCs under all three conditions of slow, medium, and fast flow velocities.

Impacts of Gravitational Mass Movements on Protective Structures—Rock Avalanches/Granular Flow

Rock avalanches and landslides lead to gravitational flow into their runout areas, which poses increasing danger to settlement areas and infrastructure in the Alpine region as a result of climate change. In recent years, a significant increase in extreme events has been registered in the Alps due to climate change. These changes in the threat to settlement areas in the Alpine region have resulted in the need for the construction of sustainable protective structures. Many structures are rigid, but others are now also increasingly flexible, e.g., net and dam structures, which are mainly earth dams with geogrids. In this study, empirical model experiments and numerical simulations were carried out to estimate the flow depth, the deposition forms and the effects on protective structures. Numerical programs usually require unknown input parameters and long computation times for a realistic simulation of the process. This study shows the results of model tests with different granular materials. Furthermore, different design approaches of different authors are presented. Finally, a design model based on the model tests of the University of Innsbruck for rigid barriers, nets and dams due to rock avalanches is presented.

Research on the Development Model of Rural Tourism Based on Multiobjective Planning and Intelligent Optimization Algorithm

In order to improve the effect of rural tourism development, this study combines multiobjective planning and intelligent optimization algorithms to analyze the development mode of rural tourism and further deals with the problem of multisensor target tracking under unknown input interference conditions by designing a tourism network consensus algorithm. The algorithm adopts a distributed multisensor fusion structure combined with consensus estimation, and each sensor first performs two-level information filtering estimation and unknown input parameter estimation locally. The experimental research results show that the rural tourism development model based on multiobjective planning and intelligent optimization algorithm proposed in this study can play an important role in the development of rural tourism.

Predicting compressive strength and behavior of ice and analyzing feature importance with explainable machine learning models

Building and using ice-related models is challenging due to the complexity of the material. A common issue, shared by both material models and (semi-)empirical approaches, is estimating unknown input parameters such as compressive strength. This is often done with additional empirical formulas which have drawbacks, e.g., they are based on a limited amount of data. Regarding material modeling, a strongly related problem is the prioritization of effects to include in the model. This is mostly done based on a subjective mix of knowledge, model purpose, and experimental studies limited to that purpose, which risks overlooking effects or interaction of effects, and limits transferability of material models to other scenarios.To tackle these issues, a hybrid approach of domain knowledge and explainable machine learning was used. A large ice test database was compiled to train machine learning models to predict compressive strength and behavior type. The machine learning models’ predictions were more accurate than existing empirical or analytical approaches and can thus be used as an alternative, though less straightforward, tool for such predictions. Further, the SHAP explainable AI method was applied to the predictions. Impact rankings of experimental parameters and interaction effects between parameters were analyzed and discussed in terms of ice mechanics. Top features were strain rate, triaxial stress state, temperature, and loading direction, but impact rankings were highly dependent on prediction target and type of ice. Few interaction effects were found. The approach adds objectivity to the prioritization of effects for material modeling and generated further insights into ice mechanics. It is also considered useful for other natural materials or generally when there is more data than knowledge.

Estimating unknown parameters of a building stock using a stochastic-deterministic-coupled approach

Interest in urban building energy modeling as a tool to evaluate effective management of urban building energy and carbon reduction policies is increasing. Since it takes a lot of time to model all buildings in the city with the lack of information about buildings, archetypes have been used. However, the deterministic method using archetypes does not reflect uncertainty with the disadvantage of not presenting design alternatives. To overcome such problems, we proposed an urban building energy modeling method using a stochastic-deterministic-coupled approach.In this paper, the identifiability of unknown parameters in a building stock was verified by analyzing the effect of the meta-model accuracy on the estimation of unknown parameters in the proposed urban building energy model. In addition, the possibility of using energy conservation measure analysis of the proposed model was evaluated.As a result, the posterior distribution of unknown input parameters was distorted when using the meta-model instead of the original building energy model in Bayesian calibration for building stock. However, the distorted posterior distribution could evaluate the effect of energy conservation measures within 0.71% errors.

Model for Reconstruction of γ-Background during Liquid Atmospheric Precipitation

With regard to reconstructing the gamma background dose rate, existing models are either empirical with limited applicability or have many unknown input parameters, which complicates their application in practice. Due to this, there is a need to search for a new approach and build a convenient, easily applicable and universal model. The paper proposes a mathematical model for reconstructing the temporal evolution of the ambient equivalent γ-radiation dose rate during rain episodes, depending on the density of radon flux from the soil surface, as well as the duration and intensity of rain. The efficiency of the model is confirmed by the high coefficient of determination (R2 = 0.81–0.99) between the measured and reconstructed ambient equivalent dose rate during periods of rain, the simulation of which was performed using Wolfram Mathematica. An algorithm was developed for restoring the dynamics of the ambient equivalent γ-radiation dose rate during rainfall. Based on the results obtained, assumptions were made where the washout of radionuclides originates. The influence of the radionuclides ratio on the increase in the total γ-radiation dose rate was investigated.

A Thermal Network Approach for a Quick and Accurate Study of Multiple Connected Switchgears

In the presented paper a thermal network approach was proposed for a quick and accurate study of the thermal performance of two interconnected and unsymmetrically energized switchgears. The method base on the full computational fluid dynamics (CFD) simulation of a single standalone switchgear which was used to provide unknown input parameters for a constructed thermal network. Next, a thermal network was compared with CFD simulation results to confirm the correctness of the network setup. Finally the network was used to build a model of two adjacent switchgears which was verified by performed heat run test. As a result of the studies it was found that proposed thermal network approach can be successfully applied for a quick and accurate study of a multiple connected switchgear systems.

Concept to Evaluate and Quantify Field Inhomogeneities in Coaxial TEM-Cells

The quality of a field probe calibration directly affects the uncertainty statement of radiated immunity tests. Therefore, precise field calibration is required. An often-used field generator is a transversal electromagnetic (TEM) cell. The IEEE Std 1309 provides three methods to calibrate a field probe. Method B, also known as the standard field method, uses a calculated reference field based on the geometry of the measurement environment and its input parameters. However, in reality, mechanical tolerances and misalignment can decrease the field quality. The results of the calculated model and the actual system may differ. Hence, this contribution proposes an approach for computing electromagnetic (EM) fields inside a TEM-cell considering mechanical tolerances and unknown input parameters. It is accomplished by projecting Maxwell’s equations onto eigenfunctions resulting in an infinite-dimensional differential equation system, the so-called generalized telegraphist’s equations (GTEs). Since this method starts with Maxwell’s equations, it can be used for a variety of applications. The proposed concept is applied on a coaxial TEM-cell with a circular cross-section with random imperfections. Based on the semi-analytical method and an input–output model for the uncertainty propagation, the combined uncertainty can be calculated following the guide to the expression of uncertainty in measurement (GUM).

Blinded challenge for precision cosmology with large-scale structure: Results from effective field theory for the redshift-space galaxy power spectrum

An accurate theoretical template for the galaxy power spectrum is key for the success of ongoing and future spectroscopic surveys. We examine to what extent the effective field theory (EFT) of large-scale structure is able to provide such a template and correctly estimate cosmological parameters. To that end, we initiate a blinded challenge to infer cosmological parameters from the redshift-space power spectrum of high-resolution mock catalogs mimicking the BOSS galaxy sample but covering a 100 times larger cumulative volume. This gigantic simulation volume allows us to separate systematic bias due to theoretical modeling from the statistical error due to sample variance. The challenge is to measure three unknown input parameters used in the simulation: the Hubble constant, the matter density fraction, and the clustering amplitude. We present analyses done by two independent teams, who have fitted the mock simulation data generated by yet another independent group. This allows us to avoid any confirmation bias by analyzers and to pin down possible tuning of the specific EFT implementations. Both independent teams have recovered the true values of the input parameters within subpercent statistical errors corresponding to the total simulation volume.

Risk Quantification in Stochastic Simulation under Input Uncertainty

When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most of the existing literature focuses on providing inferences on the mean response at the true but unknown input parameter, including point estimation and confidence interval construction. Risk quantification of mean response under input uncertainty often plays an important role in system evaluation and control, because it provides inferences on extreme scenarios of mean response in all possible input models. To the best of our knowledge, it has rarely been systematically studied in the literature. In this article, first we introduce risk measures of mean response under input uncertainty and propose a nested Monte Carlo simulation approach to estimate them. Then we develop asymptotical properties such as consistency and asymptotic normality for the proposed nested risk estimators. We further study the associated budget allocation problem for efficient nested risk simulation and finally use a sharing economy example to illustrate the importance of accessing and controlling risk due to input uncertainty.