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A system identification approach for non-intrusive reduced order modeling of radiation-induced photocurrents

Journal Article · · Foundations of Data Science
DOI:https://doi.org/10.3934/fods.2024031· OSTI ID:2394696
 [1];  [1]
  1. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
+ Show Author Affiliations
In this study, development of compact photocurrent models is currently dominated by analytical techniques that rely on physical assumptions to render the governing equations solvable in a closed form. Violation of these assumptions can reduce the accuracy of the models and/or limit their scope. In this paper we show that system identification of nonlinear state-space systems can serve as an alternative numerical basis for non-intrusive reduced order modeling of photocurrent effects. To that end we develop a compact gray box photocurrent model (GBPM) by using a state-space representation with a low-dimensional latent state equation that mimics a mathematical model for the response of an idealized class of devices to ionizing radiation. In so doing we obtain a model that learns the dynamics of a quantity of interest directly from its measurements without requiring snapshots of the internal device state or its discretized model, and can be inferred from very small data sets. To demonstrate the approach we train the GBPM using a small experimental data set for a Z5236 Zener diode and a small synthetic data set obtained by simulating a synthetic pn-junction device. We then compare the GBPMs with black box models trained on the same data and show that performance of the latter is limited by the size of the data set, while the former are able to achieve excellent performance in both the reproductive and the predictive regimes.
Research Organization:
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
NA0003525
OSTI ID:
2394696
Report Number(s):
SAND--2024-08356J
Journal Information:
Foundations of Data Science, Journal Name: Foundations of Data Science Journal Issue: 1 Vol. 7; ISSN 2639-8001
Publisher:
AIMSCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (16)

FIGURE 1(p. 6)figure FIGURE 1
FIGURE 2(p. 12)figure FIGURE 2
TABLE 1(p. 12)table TABLE 1
FIGURE 3(p. 13)figure FIGURE 3
TABLE 2(p. 14)table TABLE 2
TABLE 3(p. 14)table TABLE 3
FIGURE 4(p. 15)figure FIGURE 4
FIGURE 5(p. 15)figure FIGURE 5
FIGURE 6(p. 17)figure FIGURE 6
FIGURE 7(p. 19)figure FIGURE 7
FIGURE 8(p. 20)figure FIGURE 8
TABLE 4(p. 20)table TABLE 4
FIGURE 9(p. 21)figure FIGURE 9
TABLE 5(p. 21)table TABLE 5
FIGURE 10(p. 22)figure FIGURE 10
FIGURE 11(p. 23)figure FIGURE 11

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