A Note on Numerical Estimation of Sato’s Two-Level CES Production Function

In this paper Sato’s two-level CES production function has been estimated by nonlinear regression carried out through five different methods of optimization, namely, the Hooke-Jeeves Pattern Moves (HJPM), the Hooke-Jeeves-Quasi-Newton (HJQN), the Rosenbrock-Quasi-Newton (RQN), the Differential Evolution (DE) and the Repulsive Particle Swarm methods (RPS). The last two methods are particularly suited to optimization of extremely nonlinear (often multimodal) objective functions. While data may be containing outliers, the method of least squares has a clear disadvantage as it may be pulled by extremely small or large errors. The absolute deviation estimation of parameters is more suitable in such cases. This paper has made an attempt to estimation of parameters of Sato’s two-level CES production function by minimizing the sum of absolute errors. The minimization has been done by the five methods noted above. While the HJPM and the HJQN perform poorly at minimizing the sum of absolute deviations, the RQN performs much better. The DE and the RPS perform very well in estimating the parameters.As an exercise on real data, the German Sector "Merket-Determined Services" production function has been estimated with three inputs: Capital, Labour and Energy. The Linear Exponential (LINEX) and Sato's two-level specifications of the "Service Function" have been estimated.Sato’s productions function; CES; constant elasticity of substitution; two-level; nonlinear regression; Hooke Jeeves; Quasi-Newton; Rosenbrock; Repulsive Particle swarm; Differential Evolution; Global Optimization; Econometrics; Estimation; Outliers; Least absolute deviation; error; German Sector Market-Determined Services; Service Production function; LINEX; Linear Exponential specification

Data-driven PDE discovery with evolutionary approach

The data-driven models allow one to define the model structure in cases when a priori information is not sufficient to build other types of models. The possible way to obtain physical interpretation is the data-driven differential equation discovery techniques. The existing methods of PDE (partial derivative equations) discovery are bound with the sparse regression. However, sparse regression is restricting the resulting model form, since the terms for PDE are defined before regression. The evolutionary approach described in the article has a symbolic regression as the background instead and thus has fewer restrictions on the PDE form. The evolutionary method of PDE discovery (EPDE) is described and tested on several canonical PDEs. The question of robustness is examined on a noised data example

Learning stable and predictive structures in kinetic systems: Benefits of a causal approach

Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well established approaches focusing solely on predictive performance, especially for out-of-sample generalization

A Method of Surrogate Model Construction which Leverages Lower-fidelity Information using Space Mapping Techniques

A new method of surrogate construction is developed and applied to a pair of computational tools used in the field of aircraft design. This new method involves the pairing of data sampled from the analytical model of interest with the execution of a similar analysis performed at a lower level of fidelity. This pairing is accomplished through the use of a space mapping technique, which is a process where the design space of a lower fidelity model is aligned a higher fidelity model. The intent of applying space mapping techniques to the field of surrogate construction is to leverage the information about a system\u27s performance present at a lower fidelity level to bolster the predictive accuracy of a surrogate model based upon sampled data at a higher fidelity level. The results from the pairing of computational tools used in this research show modest gains in predictive accuracy for many of the cases investigated when compared to existing surrogate methodologie

Common pulse retrieval algorithm: a fast and universal method to retrieve ultrashort pulses

We present a common pulse retrieval algorithm (COPRA) that can be used for a broad category of ultrashort laser pulse measurement schemes including frequency-resolved optical gating (FROG), interferometric FROG, dispersion scan, time domain ptychography, and pulse shaper assisted techniques such as multiphoton intrapulse interference phase scan (MIIPS). We demonstrate its properties in comprehensive numerical tests and show that it is fast, reliable and accurate in the presence of Gaussian noise. For FROG it outperforms retrieval algorithms based on generalized projections and ptychography. Furthermore, we discuss the pulse retrieval problem as a nonlinear least-squares problem and demonstrate the importance of obtaining a least-squares solution for noisy data. These results improve and extend the possibilities of numerical pulse retrieval. COPRA is faster and provides more accurate results in comparison to existing retrieval algorithms. Furthermore, it enables full pulse retrieval from measurements for which no retrieval algorithm was known before, e.g., MIIPS measurements