Emulation Model Research Articles

Model emulators for the assessment of regional impacts and risks of climate change: A case study of rainfed maize production in Mexico

The collection of publicly available databases about climate change and its impacts on natural and human systems is unprecedented and ever-growing. However, the requirements of information can vary widely among users depending on their region, socioenvironmental context, and interests. Moreover, in the current era of active mitigation and adaptation policies, information needs are frequently not satisfied even by these massive and variated collections of databases. The development and use of emulators can help closing this information gap by allowing users to approximate the output from complex models and create user-defined experiments, without being technically or computational demanding on the user. Here, a simple emulator of the EPIC biophysical crop model is presented which is able to adequately reproduce the changes in rainfed maize and to create projections for user-defined scenarios. Moreover, it allows to produce risk measures that are not available with the original model. The proposed methodology is illustrated with a case study of rainfed maize production in Mexico for a reference emissions scenario (SSP370) and two user-defined international mitigation policy scenarios. These scenarios represent 1) current international mitigation commitments and 2) a scenario in which China withdraws from international mitigation efforts. Results showed that, under the reference scenario, climate change could have widespread consequences on rainfed production all over the country with decreases in yields reaching up to 80% in the southeast and northeast of the country. These impacts can be partially modulated by the moderately ambitious mitigation commitments assumed in recent international agreements if all countries comply. However, a potential withdraw of China from these efforts would significantly reduce any benefits from international mitigation. Under all scenarios, changes in productivity impose increasing risks for already vulnerable populations and considerable economic costs at the state and national levels. These results suggest the urgent need for critical planning for adaptation in the agricultural sector of the country.

Design of General Flux-Controlled and Charge-Controlled Memristor Emulators Based on Hyperbolic Functions

In this article, a general memristor model emulator based on the hyperbolic function is proposed. Based on the mathematical model of hyperbolic function memristor, a general circuit model emulator is designed. The general circuit can realize memristor models of different hyperbolic functions by controlling the state of switches in the circuit. The advantage of the emulator is that it can realize three different mathematical models of hyperbolic function and be easily integrated. The circuit of the hyperbolic function memristor model conforms to three basic characteristics of the mem-element, so it can be regarded as a memristor element. Finally, the model of charge-controlled memristor based on hyperbolic function is derived. The universal memristor model proposed in this article enriches the research of constructing memristor components with existing components and provides a reference for future research on mem-element emulators.

Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension

Emulation has been successfully applied across a wide variety of scientific disciplines for efficiently analysing computationally intensive models. We develop known boundary emulation strategies which utilise the fact that, for many computer models, there exist hyperplanes in the input parameter space for which the model output can be evaluated far more efficiently, whether this be analytically or just significantly faster using a more efficient and simpler numerical solver. The information contained on these known hyperplanes, or boundaries, can be incorporated into the emulation process via analytical update, thus involving no additional computational cost. In this article, we show that such analytical updates are available for multiple boundaries of various dimensions. We subsequently demonstrate which configurations of boundaries such analytical updates are available for, in particular by presenting a set of conditions that such a set of boundaries must satisfy. We demonstrate the powerful computational advantages of the known boundary emulation techniques developed on both an illustrative low-dimensional simulated example and a scientifically relevant and high-dimensional systems biology model of hormonal crosstalk in the roots of an Arabidopsis plant.

Deep Learning-Based Emulation of Radiative Transfer Models for Top-of-Atmosphere BRDF Modelling Using Sentinel-3 OLCI

The Bidirectional Reflectance Distribution Function (BRDF) defines the anisotropy of surface reflectance and plays a fundamental role in many remote sensing applications. This study proposes a new machine learning-based model for characterizing the BRDF. The model integrates the capability of Radiative Transfer Models (RTMs) to generate simulated remote sensing data with the power of deep neural networks to emulate, learn and approximate the complex pattern of physical RTMs for BRDF modeling. To implement this idea, we used a one-dimensional convolutional neural network (1D-CNN) trained with a dataset simulated using two widely used RTMs: PROSAIL and 6S. The proposed 1D-CNN consists of convolutional, max poling, and dropout layers that collaborate to establish a more efficient relationship between the input and output variables from the coupled PROSAIL and 6S yielding a robust, fast, and accurate BRDF model. We evaluated the proposed approach performance using a collection of an independent testing dataset. The results indicated that the proposed framework for BRDF modeling performed well at four simulated Sentinel-3 OLCI bands, including Oa04 (blue), Oa06 (green), Oa08 (red), and Oa17 (NIR), with a mean correlation coefficient of around 0.97, and RMSE around 0.003 and an average relative percentage error of under 4%. Furthermore, to assess the performance of the developed network in the real domain, a collection of multi-temporals OLCI real data was used. The results indicated that the proposed framework has a good performance in the real domain with a coefficient correlation (R2), 0.88, 0.76, 0.7527, and 0.7560 respectively for the blue, green, red, and NIR bands.

Robust deep neural network surrogate models with uncertainty quantification via adversarial training

AbstractSurrogate models have been used to emulate mathematical simulators of physical or biological processes for computational efficiency. High‐speed simulation is crucial for conducting uncertainty quantification (UQ) when the simulation must repeat over many randomly sampled input points (aka the Monte Carlo method). A simulator can be so computationally intensive that UQ is only feasible with a surrogate model. Recently, deep neural network (DNN) surrogate models have gained popularity for their state‐of‐the‐art emulation accuracy. However, it is well‐known that DNN is prone to severe errors when input data are perturbed in particular ways, the very phenomenon which has inspired great interest in adversarial training. In the case of surrogate models, the concern is less about a deliberate attack exploiting the vulnerability of a DNN but more of the high sensitivity of its accuracy to input directions, an issue largely ignored by researchers using emulation models. In this paper, we show the severity of this issue through empirical studies and hypothesis testing. Furthermore, we adopt methods in adversarial training to enhance the robustness of DNN surrogate models. Experiments demonstrate that our approaches significantly improve the robustness of the surrogate models without compromising emulation accuracy.

A framework for evaluating a generic virtual commissioning data model

The interoperability of different virtual commissioning applications requires a lot of manual work, as they mainly use proprietary file formats. In this case, a generic virtual commissioning data model is highly demanded to realize interoperability among different virtual commissioning applications. Based on this model, the effort of exchanging data between different virtual commissioning applications is significantly reduced. A comprehensive generic virtual commissioning data model contains a variety of virtual commissioning-related information, such as geometry, physics, kinematics, sensors, actuators, and signal connections. For such an elaborate data model, no method is contemporarily indicated to evaluate its interoperability performance. Thus, in this paper, a case-based evaluation framework is introduced to fill in this gap. With this framework, the interoperability performance of a generic virtual commissioning data model can be evaluated by conducting data exchange of several emulation models developed in different virtual commissioning applications, and each of their specific virtual commissioning modeling functions is covered in at least one of these emulation models. As an example, a case study is presented by exchanging 10 carefully selected emulation models between Siemens NX and Visual Components software applications via AutomationML data format. Based on the case study, the applicability of this evaluation framework is analyzed and discussed. Finally, conclusion and outlook are illustrated in the end.

A Neural Emulator for Uncertainty Estimation of Fire Propagation

Wildfire propagation is a highly stochastic process where small changes in environmental conditions (such as wind speed and direction) can lead to large changes in observed behaviour. A traditional approach to quantify uncertainty in fire-front progression is to generate probability maps via ensembles of simulations. However, use of ensembles is typically computationally expensive, which can limit the scope of uncertainty analysis. To address this, we explore the use of a spatio-temporal neural-based modelling approach to directly estimate the likelihood of fire propagation given uncertainty in input parameters. The uncertainty is represented by deliberately perturbing the input weather forecast during model training. The computational load is concentrated in the model training process, which allows larger probability spaces to be explored during deployment. Empirical evaluations indicate that the proposed model achieves comparable fire boundaries to those produced by the traditional SPARK simulation platform, with an overall Jaccard index (similarity score) of 67.4% on a set of 35 simulated fires. When compared to a related neural model (emulator) which was employed to generate probability maps via ensembles of emulated fires, the proposed approach produces competitive Jaccard similarity scores while being approximately an order of magnitude faster.

«ОТ БРЕВЕНЧАТОЙ ХИЖИНЫ ДО БЕЛОГО ДОМА»: БИОГРАФИИ АМЕРИКАНСКИХ ПРЕЗИДЕНТОВ В РУССКОЙ КНИГЕ ДЛЯ ДЕТСКОГО ЧТЕНИЯ ВТОРОЙ ПОЛОВИНЫ XIX ВЕКА

The article discusses biographies of American presidents written and published by Russian writers in the second half of the 19th century. The biographies were predominantly original texts, based on documents, books, and other diverse sources in English. The paper analyzes the context of the era and the specifics of America-Russia relations in the second half of the 19th century, when several American presidents (Benjamin Franklin, Abraham Lincoln, George Washington, James Garfield) were the subjects of biographical narratives created by children’s writers Maria Rostovskaya, Alexander Khrebtov, Alexander Lyubimov, Anna Munt-Valueva, Alexan- dra Annenskaya, Catherine Sysoeva, and A. Platonov. The work identifies the techniques used in the biographies to construct the image of the hero- president, and the reasons why writers turned to the figure of the American president as an ideal model for emulation in the Russian children’s book. The identified plot features help to clarify the specifics of the genre model, which used the Bildungsroman. The narrative about the “thorny” life path of the presidents allowed the writers to demonstrate to the children the hard work and will of the characters, the results of the career (presidency, fame, independence of the country), emphasized the value of these qualities for the reader-child, and thanks to the genre specifics, the psychological, moral, and social formation of the personality of the hero-American was revealed.

New Zero Power Memristor Emulator Model and Its Application in Memristive Neural Computation

We present here a simple three P-type MOSFET-based grounded memristor emulator model. The model is designed to achieve zero static power dissipation and is done so by eliminating the external DC supply i.e., no DC bias. The proposed memristor emulator model has extremely low dynamic power dissipation as well which comes to around 175 nW i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim ~67\%$ </tex-math></inline-formula> improvement compared to recent work. A mathematical analysis is carried out to present the relevance of this design. Simulations were done on Cadence Virtuoso 90 nm technology node and fingerprints of the proposed memristor emulator were obtained. The layout area occupied by the model is approx <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1154.69~\mu \text{m}^{2}$ </tex-math></inline-formula> and an external capacitor is connected to add tunability to the circuit. Furthermore, Monte Carlo and corner analysis validate the robust nature of the design. Besides, simulations have been experimentally verified using CD-4007 CMOS integrated circuit (IC) to make the design practically feasible. Furthermore, the design offers advantages such as extremely less overall power consumption and smaller chip area that could possibly pave the path for fabrication using standard CMOS technologies. At last, an application of the proposed model depicting in-memory computation through a memristor emulator crossbar array is presented in brief.

Resistive tunable memristor emulator model and its application

Nowadays, memory elements (memristor, memcapacitor, and meminductor) found a wider range of applications in various fields like analog computation, memory-based circuits, neuromorphic circuits, etc. In this article, we have proposed a high-frequency memristor emulator using current-mode analog building blocks to emulate memristive behavior. The proposed design required two analog building blocks and two passive components. It is configured in both decremental and incremental modes, and operates up to 30 MHz. The effectiveness of the emulator is verified by performing various analyses. All simulation is performed using cadence virtuoso software with a 180 nm CMOS parameter. The proposed emulator circuit is also laid out. The layout area is only 579 µm2 and it consumes 591 µW of power. The post-layout simulation has been performed at various operating frequencies and post-layout simulation results are well matched with pre-layout results. Additionally, an experiment is performed using commercially available ICs CA3080 and AD844. The experimental results well agree with the simulation results. As an application part, Chua’s oscillator, a neuromorphic adaptive learning circuit, and a high pass filter are designed. A neuromorphic adaptive learning circuit is designed to predict behavioral response over any change in environmental conditions like temperature.

Deep Learning‐Based Rapid Flood Inundation Modeling for Flat Floodplains With Complex Flow Paths

AbstractFlood inundation emulation models based on deep neural networks have been developed to overcome the computational burden of two‐dimensional (2D) hydrodynamic models. Challenges remain for flat and complex floodplains where many anabranches form during flood events. In this study, we propose a new approach to simulate the temporal and spatial variation of flood inundation for a floodplain with complex flow paths. A U‐Net‐based spatial reduction and reconstruction method (USRR) is used to find representative locations on the floodplain with complex flow paths. The water depths at these locations are simulated using one‐dimensional convolutional neural network (1D‐CNN) models, which are well‐suited to handling multivariate timeseries inputs. The flood surface is then reconstructed using the USRR method and the simulated flood depths at the representative locations. The combined 1D‐CNN and USRR method is compared with a previously developed approach based on the long short‐term memory recurrent neural network (LSTM) models and a 2D linear interpolation‐based SRR method. Compared to the LSTM model, the 1D‐CNN model is not only more accurate, but also takes less time to develop. Although both surface reconstruction methods take &lt;1 s to produce an inundation map for a specific point in time, the USRR method is more accurate than the SRR method, leading to an increase of 5.6% in the proportion of correctly detected inundation area. The combination of 1D‐CNN and USRR can detect over 95% of the inundated area simulated using a 2D hydrodynamic model but is 98 times faster.

Common Era treeline fluctuations and their implications for climate reconstructions

Two hundred years after von Humboldt's pioneering work on the upper treeline, and many fundamental studies thereafter, the rate of past elevational changes in one of the most fascinating biogeographic boundaries on our planet remains poorly understood. Here, we distinguish conceptually between realised and potential treeline positions and present an ensemble model approach to simulate mean elevational treeline fluctuations for the past 2000 years. Based on dendrochronological summer temperature records, our simple, though efficient model emulation shows that alpine treelines across the Northern Hemisphere were, on average, about 50 m higher after the Roman and medieval warm periods compared to their lowest mean position in the 1760s, which reflects the accumulated effects of repeated cold spells during the Little Ice Age. We suggest that the simulated mean differences between realised and potential treeline positions can affect the amplitude and variance of tree ring-based temperature reconstructions. Contrary to common belief, we also argue that the current offset between lower realised and much higher potential treeline positions, which appears unprecedented in the past two millennia, does not account for the yet unexplained ‘Divergence Problem’ in dendroclimatology, a decoupling between increasing temperature measurements and tree-ring chronologies since the 1970s. • Potential and realised treelines were simulated over the past 2000 years. • Treelines were highest after the Roman and medieval warm periods. • The lowest treeline positions of the Little Ice Age were in the 1760s. • Treeline fluctuations can bias tree ring-based temperature reconstructions.

Accelerating multimodal gravitational waveforms from precessing compact binaries with artificial neural networks

Gravitational waves from the coalescences of black hole and neutron stars afford us the unique opportunity to determine the sources' properties, such as their masses and spins, with unprecedented accuracy. To do so, however, theoretical models of the emitted signal that are i) extremely accurate and ii) computationally highly efficient are necessary. The inclusion of more detailed physics such as higher-order multipoles and relativistic spin-induced orbital precession increases the complexity and hence also computational cost of waveform models, which presents a severe bottleneck to the parameter inference problem. A popular method to generate waveforms more efficiently is to build a fast surrogate model of a slower one. In this paper, we show that traditional surrogate modelling methods combined with artificial neural networks can be used to build a computationally highly efficient while still accurate emulation of multipolar time-domain waveform models of precessing binary black holes. We apply this method to the state-of-the-art waveform model SEOBNRv4PHM and find significant computational improvements: On a traditional CPU, the typical generation of a single waveform using our neural network surrogate SEOBNN_v4PHM_4dq2 takes 18ms for a binary black hole with a total mass of $44 M_{\odot}$ when generated from 20Hz. In comparison to SEOBNRv4PHM itself, this amounts to an improvement in computational efficiency by two orders of magnitude. Utilising additional GPU acceleration, we find that this speed-up can be increased further with the generation of batches of waveforms simultaneously. Even without additional GPU acceleration, this dramatic decrease in waveform generation cost can reduce the inference timescale from weeks to hours.

Changes in IPCC Scenario Assessment Emulators Between SR1.5 and AR6 Unraveled.

The IPCC's scientific assessment of the timing of net-zero emissions and 2030 emission reduction targets consistent with limiting warming to 1.5°C or 2°C rests on large scenario databases. Updates to this assessment, such as between the IPCC's Special Report on Global Warming of 1.5°C (SR1.5) of warming and the Sixth Assessment Report (AR6), are the result of intertwined, sometimes opaque, factors. Here we isolate one factor: the Earth System Model emulators used to estimate the global warming implications of scenarios. We show that warming projections using AR6-calibrated emulators are consistent, to within around 0.1°C, with projections made by the emulators used in SR1.5. The consistency is due to two almost compensating changes: the increase in assessed historical warming between SR1.5 (based on AR5) and AR6, and a reduction in projected warming due to improved agreement between the emulators' response to emissions and the assessment to which it is calibrated.

Deep Gaussian Process Emulation using Stochastic Imputation

Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP training procedure that only involves optimizations of conventional GPs. In addition, predictions from DGP emulators can be made in a fast and analytically tractable manner by naturally using the closed form predictive means and variances of linked GP emulators. We demonstrate the method in a series of synthetic examples and empirical applications, and show that it is a competitive candidate for DGP surrogate inference, combining efficiency that is comparable to doubly stochastic variational inference and uncertainty quantification that is comparable to the fully-Bayesian approach. A Python package dgpsi implementing the method is also produced and available at https://github.com/mingdeyu/DGP.

A compact floating and grounded memristor model using single active element

Memristor with defined operational specifications is a challenging task that promotes innovation in emulation circuit modelling. This article presents a single active block based compact memristor emulator architecture which is suitable for both floating and grounded circuit topologies. It effectively operates in incremental/decremental modes in both the configurations. The proposed model utilizes only one Differential Voltage Current Conveyor Transconductance Amplifier (DVCCTA) as an active block along with a few passive components. It discourages the use of extra sub-circuit components like multiple active blocks, summers and multipliers. The circuit is intended to work at ±1 V of dc power supply and has a power consumption of 8.74 mW. The proposed emulator model exhibits all the characteristics of an ideal memristor with an operating frequency of 12.8 MHz. It has wide working voltage range of 50–500 mV. The memristor model is built and simulated using the TSMC 0.18 μm CMOS process parameter. The reliability and effectiveness of the proposed model is verified through non-ideal, Monte-Carlo, process corner, non-volatility and temperature variation analysis. Furthermore, the memristor prototype is constructed on the breadboard using discrete ICs AD844AN and CA3080. The experimental outcome is found in accordance with the simulation. The applicability of the proposed memristor model is verified by implementing a memristor based Schmitt trigger circuit.

A PDE-free, neural network-based eddy viscosity model coupled with RANS equations

In fluid dynamics, constitutive models are often used to describe the unresolved turbulence and to close the Reynolds averaged Navier–Stokes (RANS) equations. Traditional PDE-based constitutive models are usually too rigid to calibrate with a large set of high-fidelity data. Moreover, commonly used turbulence models are based on the weak equilibrium assumption, which cannot adequately capture the nonlocal physics of turbulence. In this work, we propose using a vector-cloud neural network (VCNN) to learn the nonlocal constitutive model, which maps a regional mean flow field to the local turbulence quantities without solving the transport PDEs. The network is strictly invariant to coordinate translation, rotation, and uniform motion, as well as ordering of the input points. The VCNN-based nonlocal constitutive model is trained and evaluated on flows over a family of parameterized periodic hills. Numerical results demonstrate its predictive capability on target turbulence quantities of turbulent kinetic energy k and dissipation ɛ. More importantly, we investigate the robustness and stability of the method by coupling the trained model back to RANS solver. The solver shows good convergence with the simulated velocity field comparable to that based on k–ɛ model when starting from a reasonable initial condition. This study, as a proof of concept, highlights the feasibility of using a nonlocal, frame-independent, neural network-based constitutive model to close the RANS equations, paving the way for the further emulation of the Reynolds stress transport models.

Clean air policies are key for successfully mitigating Arctic warming

A tighter integration of modeling frameworks for climate and air quality is urgently needed to assess the impacts of clean air policies on future Arctic and global climate. We combined a new model emulator and comprehensive emissions scenarios for air pollutants and greenhouse gases to assess climate and human health co-benefits of emissions reductions. Fossil fuel use is projected to rapidly decline in an increasingly sustainable world, resulting in far-reaching air quality benefits. Despite human health benefits, reductions in sulfur emissions in a more sustainable world could enhance Arctic warming by 0.8 °C in 2050 relative to the 1995–2014, thereby offsetting climate benefits of greenhouse gas reductions. Targeted and technically feasible emissions reduction opportunities exist for achieving simultaneous climate and human health co-benefits. It would be particularly beneficial to unlock a newly identified mitigation potential for carbon particulate matter, yielding Arctic climate benefits equivalent to those from carbon dioxide reductions by 2050.

ClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections

AbstractMany different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one‐dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench—the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection‐Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge.

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.