240 research outputs found
Potential of Deep Operator Networks in Digital Twin-enabling Technology for Nuclear System
This research introduces the Deep Operator Network (DeepONet) as a robust
surrogate modeling method within the context of digital twin (DT) systems for
nuclear engineering. With the increasing importance of nuclear energy as a
carbon-neutral solution, adopting DT technology has become crucial to enhancing
operational efficiencies, safety, and predictive capabilities in nuclear
engineering applications. DeepONet exhibits remarkable prediction accuracy,
outperforming traditional ML methods. Through extensive benchmarking and
evaluation, this study showcases the scalability and computational efficiency
of DeepONet in solving a challenging particle transport problem. By taking
functions as input data and constructing the operator from training data,
DeepONet can handle diverse and complex scenarios effectively. However, the
application of DeepONet also reveals challenges related to optimal sensor
placement and model evaluation, critical aspects of real-world implementation.
Addressing these challenges will further enhance the method's practicality and
reliability. Overall, DeepONet presents a promising and transformative tool for
nuclear engineering research and applications. Its accurate prediction and
computational efficiency capabilities can revolutionize DT systems, advancing
nuclear engineering research. This study marks an important step towards
harnessing the power of surrogate modeling techniques in critical engineering
domains
Essential Traits for the Economics of Network and ICT: Theory and Practice
【学位授与の要件】中央大学学位規則第4条第1項【論文審査委員主査】谷口 洋志 (中央大学経済学部教授)【論文審査委員副査】塩見 英治 (中央大学経済学部教授), 鳥居 昭夫 (中央大学経済学部教授), 井手 秀樹 (慶応義塾大学商学部教授)博士(経済学)中央大
Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification
It is important to accurately model materials' properties at lower length
scales (micro-level) while translating the effects to the components and/or
system level (macro-level) can significantly reduce the amount of
experimentation required to develop new technologies. Robustness analysis of
fuel and structural performance for harsh environments (such as power uprated
reactor systems or aerospace applications) using machine learning-based
multi-scale modeling and robust optimization under uncertainties are required.
The fiber and matrix material characteristics are potential sources of
uncertainty at the microscale. The stacking sequence (angles of stacking and
thickness of layers) of composite layers causes meso-scale uncertainties. It is
also possible for macro-scale uncertainties to arise from system properties,
like the load or the initial conditions. This chapter demonstrates advanced
data-driven methods and outlines the specific capability that must be
developed/added for the multi-scale modeling of advanced composite materials.
This chapter proposes a multi-scale modeling method for composite structures
based on a finite element method (FEM) simulation driven by surrogate
models/emulators based on microstructurally informed meso-scale materials
models to study the impact of operational parameters/uncertainties using
machine learning approaches. To ensure optimal composite materials, composite
properties are optimized with respect to initial materials volume fraction
using data-driven numerical algorithms
Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path Forward to Digital Twin Enabling Simulation for Accident Tolerant Fuel
The Gaussian Process (GP)-based surrogate model has the inherent capability
of capturing the anomaly arising from limited data, lack of data, missing data,
and data inconsistencies (noisy/erroneous data) present in the modeling and
simulation component of the digital twin framework, specifically for the
accident tolerant fuel (ATF) concepts. However, GP will not be very accurate
when we have limited high-fidelity (experimental) data. In addition, it is
challenging to apply higher dimensional functions (>20-dimensional function) to
approximate predictions with the GP. Furthermore, noisy data or data containing
erroneous observations and outliers are major challenges for advanced ATF
concepts. Also, the governing differential equation is empirical for
longer-term ATF candidates, and data availability is an issue. Physics-informed
multi-fidelity Kriging (MFK) can be useful for identifying and predicting the
required material properties. MFK is particularly useful with low-fidelity
physics (approximating physics) and limited high-fidelity data - which is the
case for ATF candidates since there is limited data availability. This chapter
explores the method and presents its application to experimental thermal
conductivity measurement data for ATF. The MFK method showed its significance
for a small number of data that could not be modeled by the conventional
Kriging method. Mathematical models constructed with this method can be easily
connected to later-stage analysis such as uncertainty quantification and
sensitivity analysis and are expected to be applied to fundamental research and
a wide range of product development fields. The overarching objective of this
chapter is to show the capability of MFK surrogates that can be embedded in a
digital twin system for ATF
A network biology approach evaluating the anticancer effects of bortezomib identifies SPARC as a therapeutic target in adult T-cell leukemia cells
There is a need to identify the regulatory gene interaction of anticancer drugs on target cancer cells. Whole genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by hundreds to thousands of genes that induce changes in expression. A proteasome inhibitor, bortezomib, could be a potential therapeutic agent in treating adult T-cell leukemia (ATL) patients, however, the underlying mechanism by which bortezomib induces cell death in ATL cells via gene regulatory network has not been fully elucidated. Here we show that a Bayesian statistical framework by VoyaGene® identified a secreted protein acidic and rich in cysteine (SPARC) gene, a tumor-invasiveness related gene, as a possible modulator of bortezomib-induced cell death in ATL cells. Functional analysis using RNAi experiments revealed that inhibition of the expression SPARC by siRNA enhanced the apoptotic effect of bortezomib on ATL cells in accordance with an increase of cleaved caspase 3. Targeting SPARC may help to treat ATL patients in combination with bortezomib. This work shows that a network biology approach can be used advantageously to identify the genetic interaction related to anticancer effects
Normative Consideration and Its Understandings of Law and Social System under AI and Digital Society through GDPR
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