Emulation Model Research Articles

AIRCC-Clim: A user-friendly tool for generating regional probabilistic climate change scenarios and risk measures

Complex physical models are the most advanced tools available for producing realistic simulations of the climate system. However, such levels of realism imply high computational cost and restrictions on their use for policymaking and risk assessment. Two central characteristics of climate change are uncertainty and that it is a dynamic problem in which international actions can significantly alter climate projections and information needs, including partial and full compliance of global climate goals. Here we present AIRCC-Clim, a simple climate model emulator that produces regional probabilistic climate change projections of monthly and annual temperature and precipitation, as well as risk measures, based both on standard and user-defined emissions scenarios for six greenhouse gases. AIRCC-Clim emulates 37 atm-ocean coupled general circulation models with low computational and technical requirements for the user. This standalone, user-friendly software is designed for a variety of applications including impact assessments, climate policy evaluation and integrated assessment modelling.

Performance comparison of quadratic, nonlinear, and mixed integer nonlinear MPC formulations and solvers on an air source heat pump hydronic floor heating system

There is a gap in literature on comparisons between different MPC optimal control formulations and solver choices for the same building HVAC system. Mixed Integer Nonlinear (MINL) formulations are rarely considered, despite being the most physically accurate way to represent HVAC systems. This work compares several MPC formulations, including Quadratic, Nonlinear, and MINL, applied to a case study building and investigates benefits and challenges of MINL MPCs from practical perspectives. Ten different MPC formulations were developed and implemented using Pyomo. Then, a detailed emulator model was developed using open-source Modelica libraries and used with BOPTEST to assess the performance of each MPC. Results show that convergence and control switching behaviours of MINL MPCs are sensitive to formulations, initialization approaches, solver selections, and solver parameters. Thus, they require significant effort for tuning. However, a very well-tuned MINL MPC performed similarly to successful Nonlinear MPC formulations.

Dynamical behaviors of a chaotic jerk circuit based on a novel memristive diode emulator with a smooth symmetry control

This paper presents a new chaotic memristive circuit, which is derived from the autonomous jerk circuit by replacing the first-order generalized memristive diode bridge with a simple second-order memristive emulator, which consists of six elementary electronic elements. This second-order memristive emulator model addresses the problem of changing the configuration of the diode-bridge memristor model when used for symmetry control of chaotic systems. It also allows to reduce the gap between the experimental and the numerical simulations in that during the experimental, the symmetry breaking is done by a discrete modification of the number of diodes on the branches of the diode bridge, whereas during the numerical simulations, the variation of the symmetry control parameter corresponding to the number of diodes is continuous. Thus, a new memristor emulator consisting of two diodes, two resistors and two capacitors is developed and proposes a method of control by continuous variations of a potentiometer to perform symmetry breaking. Using tools for characterizing dynamic systems such as bifurcation diagrams, Lyapunov spectrum and phase portraits, to name just a few, it is shown how the new memristive emulator model can be used to check symmetry. The first major contribution of this work is the control of the symmetry of a jerk oscillator when the resistors of the memristive emulator take different values without modifying its structure. The second major contribution of this work is the ability to change the nature of an attractor as a function of its position in phase space without changing the nature of its associated symmetric attractor, which is demonstrated using phase portraits and initial condition diagrams. An electronic circuit is presented showing how symmetry control is achieved by varying a potentiometer. And an experimental study is performed to validate the results.

Errors in Simple Climate Model Emulations of Past and Future Global Temperature Change

AbstractClimate model emulators are widely used to generate temperature projections for climate scenarios, including in the recent Intergovernmental Panel on Climate Change Sixth Assessment Report. Here we evaluate the performance of a two‐layer energy balance model in emulating historical and future temperature projections from Coupled Model Intercomparison Project Phase 6 models. We find that emulation errors can be large (>0.5°C for SSP2‐4.5) and differ markedly between climate models, forcing scenarios and time periods. Errors arise in emulating the near‐surface temperature response to both greenhouse gas and aerosol forcing; in some periods the errors due to these forcings oppose one another, giving the spurious impression of better emulator performance. Climate feedbacks are assumed constant in the emulator, introducing time‐varying or state dependent feedbacks may reduce prediction errors. Close emulations can be produced for a given period but, crucially, this does not guarantee reliable emulations of other scenarios and periods. Therefore, rigorous out‐of‐sample evaluation is necessary to characterize emulator performance.

Conceptualizing the next patient interaction: A clinical skillset using an emulation model in critical thinking.

The next patient interaction is logical, essential, and largely done intuitively. To design and test a succinct learning guide for student guidance and student-faculty interaction in conceptualizing the next patient interaction. In 2021 and 2022, faculty scored questions from 1-5: Recognize deviation from the ideal? Articulate how and how much the situation deviated? Gage consequences of the situation? Assess own capabilities? To what extent does the student have a clear grasp of the procedural outcome? Faculty were also given open-ended questions. Forty-eight reports were completed, 25 D3 and 23 D4. Three hundred thirty-five faculty entries were made in the questions calling for a 1-5 response out of a possible 336 responses. Statistically significant differences were noted. Students were better able to recognize the situation as different from ideal than to articulate how and how much the situation deviated. Students were better able to grasp how and how much this situation differs from the ideal than to assess own capabilities. D4 students were better able to recognize deviation from the ideal and to articulate how and how much the situation deviated than were D3 students. For open-ended questions, more students were scored as "Prepared" than were scored as "Unsure" and "Missed" combined. The exercise is seen as a succinct and constructive (nonjudgmental) path to guide the student's conceptualization of the next patient encounter before the encounter begins. Next steps will be incremental for wider use in a clinical teaching environment.

Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach

Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). In the longer term, the final aim of this tool is to enlarge the high-resolution RCM simulation ensembles at low cost to explore better the various sources of projection uncertainty at local scale. Using a neural network, we build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12 km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM, particularly how the RCM refines the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a substantial computational benefit of running the emulator rather than the RCM, since training the emulator takes about 2 h on GPU, and the prediction takes less than a minute. However, further work is needed to improve the reproduction of some temperature extremes, the climate change intensity and extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

Dynamic Model and Converter-Based Emulator of a Data Center Power Distribution System

Data centers have become a widespread power electronics (PE) load, which has significant impact on the power grid. In order to investigate the data center load characteristics, this article proposes a complete dynamic model for a typical data center ac power distribution system. A generalized model with mode transition is proposed to coordinate different power stages in the data center power system. Meanwhile, to help evaluate the grid dynamic performance and transient stability, an all-in-one load data center power emulator is developed on a reconfigurable PE converter-based hardware testbed (HTB). The dynamic power model is digitized and simplified to be implemented in two local voltage source inverters on the HTB. This proposed data center power emulator has been verified experimentally in a regional network. Dynamic performances during voltage sag events and server load variations are emulated and discussed. The article details the design, development, and verification of the data center model and power emulator. The proposed model and emulator provide an effective, easy-to-use tool to better design data centers and study the interaction with the power system.

MESMER-M: an Earth system model emulator for spatially resolved monthly temperature

Abstract. The degree of trust placed in climate model projections is commensurate with how well their uncertainty can be quantified, particularly at timescales relevant to climate policy makers. On inter-annual to decadal timescales, model projection uncertainty due to natural variability dominates at the local level and is imperative to describing near-term and seasonal climate events but difficult to quantify owing to the computational constraints of producing large ensembles. To this extent, emulators are valuable tools for approximating climate model runs, allowing for the exploration of the uncertainty space surrounding selected climate variables at a substantially reduced computational cost. Most emulators, however, operate at annual to seasonal timescales, leaving out monthly information that may be essential to assessing climate impacts. This study extends the framework of an existing spatially resolved, annual-scale Earth system model (ESM) emulator (MESMER, Beusch et al., 2020) by a monthly downscaling module (MESMER-M), thus providing local monthly temperatures from local yearly temperatures. We first linearly represent the mean response of the monthly temperature cycle to yearly temperatures using a simple harmonic model, thus maintaining month-to-month correlations and capturing changes in intra-annual variability. We then construct a month-specific local variability module which generates spatio-temporally correlated residuals with yearly temperature- and month-dependent skewness incorporated within. The emulator's ability to capture the yearly temperature-induced monthly temperature response and its surrounding uncertainty due to natural variability is demonstrated for 38 different ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The emulator is furthermore benchmarked using a simple gradient-boosting-regressor-based model trained on biophysical information. We find that while regional-scale, biophysical feedbacks may induce non-uniformities in the yearly to monthly temperature downscaling relationship, statistical emulation of regional effects shows comparable skill to the more physically informed approach. Thus, MESMER-M is able to statistically generate ESM-like, large initial-condition ensembles of spatially explicit monthly temperature fields, providing monthly temperature probability distributions which are of critical value to impact assessments.

Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study.

Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)-derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE). In this substudy of a prospective, multicenter study, patients from 3 institutions (n=727) formed an internal cohort, and the fourth institution was reserved as an external test set (n=518). A previously validated patient similarity analysis model was used for labeling the patients as low-/high-risk phenogroups. These labels were utilized for training an ECG-derived deep neural network model to predict MACE risk per phenogroup. After 5-fold cross-validation training, the model was tested on the reserved external dataset. Our ECG-derived model showed robust classification of patients, with area under the receiver operating characteristic curve of 0.86 (95% CI: 0.79-0.91) and 0.84 (95% CI: 0.80-0.87), sensitivity of 80% and 76%, and specificity of 88% and 75% for the internal and external test sets, respectively. The ECG-derived model demonstrated an increased probability for MACE in high-risk vs low-risk patients (21% vs 3%; P<0.001), which was similar to the echo-trained model (21% vs 5%; P<0.001), suggesting comparable utility. This novel ECG-derived machine learning model provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high risk of MACE.

Multifidelity Computer Model Emulation with High-Dimensional Output: An Application to Storm Surge

AbstractHurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude due to the nature of the system. The threat posed by surge makes greater fidelity highly desirable, however, such models and their high-volume output tend to come at great computational cost, which can make detailed study of coastal flood hazards prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model to predict highly accurate storm surges in a computationally efficient way over a large spatial domain. This emulator has the capability of predicting storm surges as accurately as a high-fidelity computer model given any storm characteristics over a large spatial domain.

Efficient emulation of relativistic heavy ion collisions with transfer learning

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models ("emulators") can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one model emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.

Research on sea clutter model of emulating aircraft motion based on shore-based multichannel radar

ABSTRACT Space-time adaptive processing (STAP) is an important clutter suppression technique in airborne multichannel radar. It can be a challenging problem to apply STAP to the maritime airborne radar without knowledge of sea clutter space-time coupling characteristics. The radar sea clutter data with space-time coupling characteristics can be obtained by airborne multichannel radar, but the flight testing is very expensive. A cost-effective technique is presented for emulating airborne motion based on shore-based multichannel radar. The technique is implemented by the same array shared with transmit and receive antenna, where the transmit array uses an inverse displaced phase centre antenna to emulate platform motion and the receive array is used for receiving data. Based on the above technique, a sea clutter model of aircraft motion emulation is proposed. Then, we verify the model by quantitatively comparing the number of eigenvalue and the spread width of the space-time power spectrum between the simulated and measured data. The result shows that the eigenvalue number and the spread width of 3 dB clutter ridge of measured data are more than 79.5% of that belonging to simulated data. It means that the simulated radar sea clutter data generated by this model can better fit the measured data under sea states classes 1–4, which can provide support for the analysis of sea clutter space-time characteristics.

From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: coupling of MAGICC (v7.5.1) and MESMER (v0.8.3)

Abstract. Producing targeted climate information at the local scale, including major sources of climate change projection uncertainty for diverse emissions scenarios, is essential to support climate change mitigation and adaptation efforts. Here, we present the first chain of computationally efficient Earth system model (ESM) emulators that allow for the translation of any greenhouse gas emission pathway into spatially resolved annual mean temperature anomaly field time series, accounting for both forced climate response and natural variability uncertainty at the local scale. By combining the global mean, emissions-driven emulator MAGICC with the spatially resolved emulator MESMER, ESM-specific and constrained probabilistic emulated ensembles can be derived. This emulator chain can hence build on and extend large multi-ESM ensembles such as the ones produced within the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The main extensions are threefold. (i) A more thorough sampling of the forced climate response and the natural variability uncertainty is possible, with millions of emulated realizations being readily created. (ii) The same uncertainty space can be sampled for any emission pathway, which is not the case in CMIP6, where only a limited number of scenarios have been explored and some of the most societally relevant strong mitigation scenarios have been run by only a small number of ESMs. (iii) Other lines of evidence to constrain future projections, including observational constraints, can be introduced, which helps to refine projected ranges beyond the multi-ESM ensembles' estimates. In addition to presenting results from the coupled MAGICC–MESMER emulator chain, we carry out an extensive validation of MESMER, which is trained on and applied to multiple emission pathways for the first time in this study. By coupling MAGICC and MESMER, we pave the way for rapid assessments of any emission pathway's regional climate change consequences and the associated uncertainties.

Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one full simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we developed a statistical emulation framework for land surface models, enabling fast predictions at a high resolution. To do so, our emulation framework acknowledges, and makes use of, the multitude of contextual data that are often fed into land surface models (sometimes called forcing data, or driving data), such as air temperature or various soil properties. We use The Joint UK Land Environment Simulator (JULES) as a case study for this methodology, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. The JULES is perhaps one of the most complex land surface models and so our success here suggests incredible gains can be made for all types of land surface model.

Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations

Computer models are widely used to help make decisions about real-world systems. As computer models of large and complex systems can have long run-times and high-dimensional input spaces, it is often necessary to use emulation to assess uncertainties in computer model output. This paper presents methodology for emulation of complex computer models motivated by a real-world example in energy policy. The computer model studied is an economic model of investment in electricity generation in Great Britain. The computer model was used to select parameters in a government policy designed to incentivize investment in renewable technologies to meet government targets. Limited computing time meant that few runs of the computer model were available to fit an emulator. The statistical methodology developed was therefore focused on accurately capturing the uncertainty in computer model output arising from the small number of available model runs. A varying coefficient emulator is proposed to model uncertainty in model output when extrapolating away from model runs. To maximize use of the small number of runs available, this varying coefficient emulator is paired with a criterion-based procedure for design selection.

Demonstration of combined reduced order model and deep neural network for emulation of a time-dependent reactor transient

In this paper, a Reduced-Order Model (ROM) was constructed to approximate nuclear reactor neutronic transient behaviour. The ROM was developed by first using the Principal-Component Analysis (PCA) to reduce the output space of the response of interest, i.e, the time-dependent reactor power. A Deep-Neural Network (DNN) is then employed to build a surrogate model that maps the model inputs, i.e, the kinetic data, to the reduced-order output. While many previous applications of machine learning have focused on using algorithms to generate the response of singular Figures of Merit (such as peak clad or peak fuel temperatures over an entire transient) relatively few works have developed methods that can replicate the transient output of computer codes over the duration of the simulations. The difficulty in using machine learning to reconstruct the entire transient response is that, given the duration of the transient and the relatively fine time steps used, the output space can be very large thus making standard machine learning methods difficult to apply. In this work, PCA is first used to identify the key components of the output responses such that DNN can then be used to map the input parameters to the Principal component time-dependent behaviour. Then the neural network can reconstruct these components and emulate the output transient response based on the input parameters selected. Thus the methodology can provide time-dependent output results rather than single figures of merit. In this work, the LRA benchmark was used as a demonstration problem. The Detran time-dependent diffusion solver was used to generate the training and test data for a power control-rod ejection scenario. The results show that the combined ROM-DNN methodology is able to reconstruct the output power trajectory with high accuracy and with significantly reduced computational overhead.

Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation

Energy-economic simulation models with high levels of detail, high time resolutions, or large populations (e.g., distribution networks, households, electric vehicles, energy communities) are often limited due to their computational complexity. This paper introduces a novel methodology, combining cluster-based time series aggregation and sampling methods, to efficiently emulate simulation models using machine learning and significantly reduce both simulation and training time. Machine learning-based emulation models require sufficient and high-quality data to generalize the dataset. Since simulations are computationally complex, their maximum number is limited. Sampling methods come into play when selecting the best parameters for a limited number of simulations ex ante. This paper introduces and compares multiple sampling methods on three energy-economic datasets and shows their advantage over a simple random sampling for small sample-sizes. The results show that a k-means cluster sampling approach (based on unsupervised learning) and adaptive sampling (based on supervised learning) achieve the best results especially for small sample sizes. While a k-means cluster sampling is simple to implement, it is challenging to increase the sample sizes if the emulation model does not achieve sufficient accuracy. The iterative adaptive sampling is more complex during implementation, but can be re-applied until a certain accuracy threshold is met. Emulation is then applied on a case study, emulating an energy-economic simulation framework for peer-to-peer pricing models in Germany. The evaluated pricing models are the “supply and demand ratio” (SDR) and “mid-market rate pricing” (MMR). A time series aggregation can reduce time series data of municipalities by 99.4% with less than 5% error for 98.2% (load) and 95.5% (generation) of all municipalities and hence decrease the simulation time needed to create sufficient training data. This paper combines time series aggregation and emulation in a novel approach and shows significant acceleration by up to 88.9% of the model’s initial runtime for the simulation of the entire population of around 12,000 municipalities. The time for re-calculating the population (e.g., for different scenarios or sensitivity analysis) can be increased by a factor of 1100 while still retaining high accuracy. The analysis of the simulation time shows that time series aggregation and emulation, considered individually, only bring minor improvements in the runtime but can, however, be combined effectively. This can significantly speed up both the simulation itself and the training of the emulation model and allows for flexible use, depending on the capabilities of the models and the practitioners. The results of the peer-to-peer pricing for approximately 12,000 German municipalities show great potential for energy communities. The mechanisms offer good incentives for the addition of necessary flexibility.

Minimal CMIP Emulator (MCE v1.2): a new simplified method for probabilistic climate projections

Abstract. Climate model emulators have a crucial role in assessing warming levels of many emission scenarios from probabilistic climate projections based on new insights into Earth system response to CO2 and other forcing factors. This article describes one such tool, MCE, from model formulation to application examples associated with a recent model intercomparison study. The MCE is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters for probabilistic projections are generated from statistical models and constrained with a Metropolis–Hastings independence sampler. Some of the model parameters associated with CO2-induced warming have a covariance structure, as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). Perturbed ensembles can cover the diversity of CMIP models effectively, and they can be constrained to agree with several climate indicators such as historical warming. The model's simplicity and resulting successful calibration imply that a method with less complicated structures and fewer control parameters offers advantages when building reasonable perturbed ensembles in a transparent way. Experimental results for future scenarios show distinct differences between CMIP-consistent and observation-consistent ensembles, suggesting that perturbed ensembles for scenario assessment need to be properly constrained with new insights into forced response over historical periods.

Developing simulation approaches: a simple case of emulation for logic validation using tecnomatix plant simulation

Over the years, simulation and simulation tools have provided industry and many other sectors with an incredible number of options for the improvement of their processes. Together with the development of new technologies and industrial practices, simulation has also evolved leading to the new solution approaches, which are used in the context of the current industrial era or Industry 4.0. The present paper presents a take on the evolution of simulation leading up until Simulation 4.0 and some of its defining elements. Special emphasis is made in some of the approaches in this context as it is the case of the Emulation models or Digital Twins and the Virtual Commissioning ones. The paper also includes a simple application example that presents a developing simulation approach as it is the case of the emulation for logic validation. This is done from the optics of Tecnomatix Plant Simulation, which is a promising fast evolving tool offering not only great 3D capabilities but also sound analytics of the simulated instances.

Responsibility of major emitters for country-level warming and extreme hot years

The contributions of single greenhouse gas emitters to country-level climate change are generally not disentangled, despite their relevance for climate policy and litigation. Here, we quantify the contributions of the five largest emitters (China, US, EU-27, India, and Russia) to projected 2030 country-level warming and extreme hot years with respect to pre-industrial climate using an innovative suite of Earth System Model emulators. We find that under current pledges, their cumulated 1991–2030 emissions are expected to result in extreme hot years every second year by 2030 in twice as many countries (92%) as without their influence (46%). If all world nations shared the same fossil CO2 per capita emissions as projected for the US from 2016–2030, global warming in 2030 would be 0.4 °C higher than under actual current pledges, and 75% of all countries would exceed 2 °C of regional warming instead of 11%. Our results highlight the responsibility of individual emitters in driving regional climate change and provide additional angles for the climate policy discourse.