Calibration Of Physical Models Research Articles

Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates

In the northeast U.S., resource managers commonly apply 7-day, 10-year (7Q10) low flow estimates for protecting aquatic species in streams. In this paper, the efficacy of process-based hydrologic models is evaluated for estimating 7Q10s compared to the United States Geological Survey's (USGS) widely applied web-application StreamStats, which uses traditional statistical regression equations for estimating extreme flows. To generate the process-based estimates, the USGS's National Hydrologic Modeling (NHM-PRMS) framework (which relies on traditional rainfall-runoff modeling) is applied with 36 years of forcings from the Daymet climate dataset to a representative sample of ninety-four unimpaired gages in the Northeast and Mid-Atlantic U.S. The rainfall-runoff models are calibrated to the measured streamflow at each gage using the recommended NHM-PRMS calibration procedure and evaluated using Kling-Gupta Efficiency (KGE) for daily streamflow estimation. To evaluate the 7Q10 estimates made by the rainfall-runoff models compared to StreamStats, a multitude of error metrics are applied, including median relative bias (cfs/cfs), Root Mean Square Error (RMSE) (cfs), Relative RMSE (RRMSE) (cfs/cfs), and Unit-Area RMSE (UA-RMSE) (cfs/mi2). The calibrated rainfall-runoff models display both improved daily streamflow estimation (median KGE improving from 0.30 to 0.52) and 7Q10 estimation (smaller median relative bias, RMSE, RRMSE, and UA-RMSE, especially for basins larger than 100 mi2). The success of calibration is extended to ungaged locations using the machine learning algorithm Fuzzy C-Means (FCM) clustering, finding that traditional K-Means clustering (FCM clustering with no fuzzification factor) is the preferred method for model regionalization based on (1) Silhouette Analysis, (2) daily streamflow KGE, and (3) 7Q10 error metrics. The optimal rainfall-runoff models created with clustering show improvement for daily streamflow estimation (a median KGE of 0.48, only slightly below that of the calibrated models at 0.52); however, these models display similar error metrics for 7Q10 estimation compared to the uncalibrated models, neither of which provide improved error compared to the statistical estimates. Results suggest that the rainfall-runoff models calibrated to measured streamflow data provide the best 7Q10 estimation in terms of all error metrics except median relative bias, but for all models applicable to ungaged locations, the statistical estimates from StreamStats display the lowest error metrics in every category.

Uniaxial constitutive model for fiber reinforced concrete: A physics-based data-driven framework

Fiber reinforced concrete (FRC) has improved strength and ductility, making it suitable for a wider range of engineering structures. The development of a constitutive model is crucial for analyzing mechanical behavior of these structures, while this still remains challenging as the complex composition of FRC makes it difficult to formulate explicit relationships among a range of critical material parameters. With the latest development of data and digital technologies, data-driven approaches have emerged as a powerful alternative that are capable of solving advanced and complex engineering problems. Developing data-driven methods based on objective data analysis and decision-making to predict mechanical behavior of engineering structures is an important direction of such development. In this paper, a uniaxial constitutive model of fiber-reinforced concrete is developed by a physics-based data-driven framework. The framework consists of three important parts, including construction of experimental database, parameters calibration of physical models, and implementation of neural network. From the experimental data collected from published literature, an experimental database is built first. A physical model is proposed by modifying the uniaxial constitute model for normal concrete, so that it is more convenient and straightforward to consider the effect of fibers on fiber reinforced concrete subjected to compression, tension and repeat loading conditions. The parameters of the model are calibrated against experimental data by the swarm intelligence optimization algorithms. With the calibrated parameters of the physical model, a Fully Connected Neural Network (FCNN) is trained to be used to predict the physical parameters of fiber reinforced concrete. By comparing with independent experimental data, the proposed fiber-reinforced concrete uniaxial constitutive model constructed using the physics-based data-driven framework can accurately predict the stress–strain relationships of a range of FRC, which suggests that the FRC material model can be used in the numerical simulation and design of FRC components and structures. In addition, the proposed model is applicable to multiple loading conditions.

SoulNet: ultrafast optical source optimization utilizing generative neural networks for advanced lithography

An optimized source has the ability to improve the process window during lithography in semiconductor manufacturing. Source optimization is always a key technique to improve printing performance. Conventionally, source optimization relies on mathematical–physical model calibration, which is computationally expensive and extremely time-consuming. Machine learning could learn from existing data, construct a prediction model, and speed up the whole process. We propose the first source optimization process based on autoencoder neural networks. The goal of this autoencoder-based process is to increase the speed of the source optimization process with high-quality imaging results. We also make additional technical efforts to improve the performance of our work, including data augmentation and batch normalization. Experimental results demonstrate that our autoencoder-based source optimization achieves about 105 × speed up with 4.67% compromise on depth of focus (DOF), when compared to conventional model-based source optimization method.

3D quantification of structure and hemodynamics in the spiral arteries of humans in early pregnancy

Often times, the development of physical models of materials behavior is hindered not only by the incompleteness of the theoretical approach, but also by uncertainties that arise from limitations in the experimental observations used to validate and calibrate these models. In this work, we present a Bayesian framework for both the calibration of physical models as well as the quantification of likely uncertainty in experimental observations and apply it to a model for the plastic response of multi-phase Transformation Induced Plasticity (TRIP) steels. The model is based on a formulation of irreversible thermodynamics of plastic deformation and accounts for the presence of multiple phases through homogenization theories based on the iso-work approximation. Bayesian calibration through Metropolis-Hastings Markov Chain Monte Carlo has been used to calibrate a subset of the model parameters against experimental data sequentially and simultaneously. The calibrated parameters obtained from sequential training were in turn used to assess the uncertainty in a subset of experimental data—i.e. phase volume fractions—used as input to the models themselves. The viability of the calibration approach has also been examined using synthetic data generated from simultaneous calibrated model.

RTN in Scaled Transistors for On-Chip Random Seed Generation

Random numbers play a vital role in cryptography, where they are used to generate keys, nonce, one-time pads, and initialization vectors for symmetric encryption. The quality of random number generator (RNG) has significant implications on vulnerability and performance of these algorithms. A pseudo-RNG uses a deterministic algorithm to produce numbers with a distribution very similar to uniform. True RNGs (TRNGs), on the other hand, use some natural phenomenon/process to generate random bits. They are nondeterministic, because the next number to be generated cannot be determined in advance. In this paper, a novel on-chip noise source, random telegraph noise (RTN), is exploited for simple and reliable TRNG. RTN, a microscopic process of stochastic trapping/detrapping of charges, is usually considered as a noise and mitigated in design. Through physical modeling and silicon measurement, we demonstrate that RTN is appropriate for TRNG, especially in highly scaled MOSFETs. Due to the slow speed of RTN, we purpose the system for on-chip seed generation for random number. Our contributions are: 1) physical model calibration of RTN with comprehensive 65- and 180-nm transistor measurements; 2) the scaling trend of RTN, validated with silicon data down to 28 nm; 3) design principles to achieve 50% signal probability by using intrinsic RTN physical properties, without traditional postprocessing algorithms, the generated sequence passes the National Institute of Standards and Technology (NIST) tests; and 4) solutions to manage realistic issues in practice, including multilevel RTN signal, robustness to voltage and temperature fluctuations and the operation speed.

Using Bayesian framework to calibrate a physically based model describing strain-stress behavior of TRIP steels

Often times, the development of physical models of materials behavior is hindered not only by the incompleteness of the theoretical approach, but also by uncertainties that arise from limitations in the experimental observations used to validate and calibrate these models. In this work, we present a Bayesian framework for both the calibration of physical models as well as the quantification of likely uncertainty in experimental observations and apply it to a model for the plastic response of multi-phase Transformation Induced Plasticity (TRIP) steels. The model is based on a formulation of irreversible thermodynamics of plastic deformation and accounts for the presence of multiple phases through homogenization theories based on the iso-work approximation. Bayesian calibration through Metropolis-Hastings Markov Chain Monte Carlo has been used to calibrate a subset of the model parameters against experimental data sequentially and simultaneously. The calibrated parameters obtained from sequential training were in turn used to assess the uncertainty in a subset of experimental data—i.e. phase volume fractions—used as input to the models themselves. The viability of the calibration approach has also been examined using synthetic data generated from simultaneous calibrated model.

On the Statistical Calibration of Physical Models

ABSTRACTWe introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data.

NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-state and Variable Temperature Operation

Summary Two NARX-type neural networks are developed for modelling nonlinear dynamic characteristics of passive twin-tube hydraulic dampers used in vehicle suspension systems. Quasi-isothermal and variable temperature NARX models are rigorously tested and compared with a state-of-the-art physical model proposed by Duym and Reybrouck (1998) and Duym (2000). Measured damper data, generated under isothermal and temperature varying conditions, is used for NARX training, physical model calibration, and predictive comparisons. Test kinematics include high amplitude sinusoidal displacements up to 14 Hz, and realistic random road profiles. The NARX models are trained via ‘teacher forcing’ and the feedforward backpropagation algorithm using both ‘Early Stopping’ and Bayesian Regularisation. Stable network design is also examined using the minimum posterior prediction error as the criterion for selecting a good network from a small number of tests. Calibration of the physical model proves highly complicated owing to considerable nonlinearity-in-the-parameters, requiring use of Sequential Quadratic Programming with an implicitly nonlinear constraint. The paper shows that NARX neural network modelling is vastly superior in terms of calibration efficiency, and prediction times, whilst offering roughly similar, if not better, model accuracy.

A formant filtered physical model for wind instruments

We report on our research concerning the calibration of physical models for sound synthesis. We combine waveguide physical modeling synthesis with formant filtering, by dividing the nonlinear description of the reed mechanism into a nonlinear part and an input-dependent linear filter. We elaborate on the calibration of the model and assess its performance by comparing it to a single-reed, cylindrical bore instrument, the clarinet.

A new Swiss test-site for avalanche experiments in the Vallée de la Sionne/Valais

A new Swiss test-site for avalanche experiments has been built to study the overall dynamic behaviour of dense-flow and powder-snow avalanches and to measure avalanche impact forces along their path. An important application of the data gathered is for the verification and calibration of physical models and of computer simulation programs. For the impact studies, a wall, a pylon of circular section, a girder mast and a section of the roof of a road protection gallery are placed within the avalanche track and equipped with force transducers and pressure and strain gauges. The dynamic behaviour of the avalanches is measured by Doppler- and FMCW-radars. Snow mass balances are established by photogrammetry. The avalanches are artificially released. For many years, the Swiss Federal Institute for Snow and Avalanche Research used a test-site in the Canton Grison to study the dynamic behaviour of dense-flow avalanches. The limited number of successful experiments at this site forced the institute to examine several alternatives for an avalanche test-site. It was finally decided that a site in the Canton Valais, with several potential avalanche tracks with more than 1000 m of potential difference in altitude, was most suitable for our purpose. After several years of planning for the test-site, the civil engineering construction work started in summer 1997, followed by the installation of sensor devices and the data acquisition, transfer and storage system. In December 1997, the test-site was ready for experiments.