Fast Probabilistic Forecasting of Oil Production using Monte Carlo Simulations on Data Driven Acquisition of Decline-Curve Parameter Distributions

Abstract Water quality management requires a profound understating of future variations of surface and groundwater qualities for assessment and planning for human consumption, industrial, and irrigation purposes. In this regard, mathematical models, such as Box-Jenkins time series models, Bayesian time series models, and data-driven models are available for future prediction of water quality. However, the uncertainty associated with forecasting is one of the main problems of using these models towards water quality and future planning. In the present work, the uncertainty of the Adaptive Neuro-Fuzzy Inference System, based on Fuzzy c-means clustering, (ANFIS-FCMC) (genfis 3) model is quantified to analyze and predict Sodium Adsorption Rate(SAR) of water of Aras, Sepid-Rud, and Karun Rivers by using Monte Carlo simulations. The results indicate the combined standard and the expanded uncertainty simulated for SAR of Aras River water are 0.58 and1.16, respectively, and the gap is 2 .412 ±1.1622. Also, the combined standard and the expanded uncertainty simulated for SAR of Spid-Rud River water were1.11 and 2.22, respectively, and the gap is equal to 2 .235 ±2.22. Furthermore, the combined standard and the expanded uncertainty simulated for SAR of Aras River water are 2.063, and 4.126, respectively, and the gap is 4.79 ±4.126. Finally, the minimum uncertainty happened to predict SAR of Aras River using ANFIS-FCMC (genfis3) model and maximum SAR uncertainty belong to Karun River.

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A Data-driven Surrogate Model for Work Computation of a Periodically Forced Half-Sarcomere

10.1101/2020.03.09.984310 ◽

2020 ◽

Author(s):

Samuel H. Rudy ◽

C. David Williams ◽

J. Nathan Kutz ◽

Thomas L. Daniel

Keyword(s):

Monte Carlo ◽

Monte Carlo Simulations ◽

Surrogate Model ◽

Computational Cost ◽

Data Driven ◽

State Variables ◽

Molecular Scale ◽

Length Changes ◽

Using Data ◽

Space Data

AbstractMuscle force generation follows from molecular scale interactions that drive macroscopic behaviors and macroscopic processes that influence those at the molecular scale. A particuarly challenging issue is that models at the molecular level of organization are often quite difficult to apply to larger spatial scales. This is particularly true of moleuclar models driven by Monte-Carlo simulations. This challenge of multiscale dynamics requires methods to extract reduced order behaviors from detailed high-dimensional simulations. In this work we present a novel deterministic simulation method yielding accurate predictions of force-length behaviors of contracting muscle sarcomeres undergoing periodic length changes (work loops). The model maintains interpretability by tracking macroscopic state variables throughout the simulation while using data-driven representations of dynamics. Parameters of the data-driven dynamics are learned from trajectories from Monte-Carlo simulations of a half-sarcomere. Our method significantly reduces computational cost by tracking the state of the sarcomere in a course grained set of variables while maintaining accurate prediction of macroscopic level observables and time series for course grained variables. This allows for rapid sampling of the model’s output and builds towards the ability to scale to multiple-sarcomere simulations.Author SummaryWe develop a data-driven surrogate model for the dynamics of the half-sarcomere. This model achieves the same behavior with respect to force traces as more sophisticated Monte Carlo models at a substantially lower computational cost. The model is built by finding a course grained description of the full state space of the Monte Carlo simulation and learning dynamical models on the course grained space. Data-driven representations of the dynamics in the course grained space are trained using data from the full model. Data-driven models for forcing are also learned, and the result fed back into the dynamics. In doing so, the model seeks to replicate the effects of filament compliance on macro scale dynamics without explicitly tracking micro scale features. We withhold some input parameter regimes and demonstrate accurate reconstruction of course grained state and force traces using the data-driven model and given only knowledge of the initial condition and input. This work allows for faster computation of the forcing behavior of the half-sarcomere, as well as consistent representations of the course grained state variables. It is therefore promising as a step towards multi-sarcomere or even tissue scale models of skeletal muscle.

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Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100164854 ◽

1996 ◽

Vol 54 ◽

pp. 478-479

Author(s):

Matthew T. Johnson ◽

Ian M. Anderson ◽

Jim Bentley ◽

C. Barry Carter

Keyword(s):

Monte Carlo ◽

Quantitative Analysis ◽

Monte Carlo Simulations ◽

Cross Section ◽

Spatial Resolution ◽

Low Voltage ◽

Magnesium Ferrite ◽

X Ray ◽

Interaction Volume ◽

Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

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