scholarly journals Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk

2022 ◽  
pp. 108280
Author(s):  
John D. Jakeman ◽  
Drew P. Kouri ◽  
J. Gabriel Huerta
Keyword(s):  
Surrogate Modeling ◽  
Measures Of Risk

Uncertain Meanings of Risk

2018 ◽  
Author(s):  
Natalia Besedovsky
Keyword(s):  
Financial Markets ◽  
Credit Rating ◽  
Traditional Approach ◽  
Credit Ratings ◽  
Assessment Practices ◽  
Structured Finance ◽  
Rating Agencies ◽  
Measures Of Risk ◽  
Methodological Approaches ◽  
Ontological Uncertainty

This chapter studies calculative risk-assessment practices in credit rating agencies. It identifies two fundamentally different methodological approaches for producing ratings, which in turn shape the respective conceptions of credit risk. The traditional approach sees ‘risk’ as an only partially calculable and predictable set of hazards that should be avoided or minimized. This approach is particularly evident in the production of country credit ratings and gives rise to ordinal rankings of risk. By contrast, structured finance rating practices conceive of ‘risk’ as both fully calculable and controllable; they construct cardinal measures of risk by assuming that ontological uncertainty does not exist and that models can capture all possible events in a probabilistic manner. This assumption—that uncertainty can be turned into measurable risk—is a necessary precondition for structured finance securities and has become an influential imaginary in financial markets.

scholarly journals Machine learning and simulation-based surrogate modeling for improved process chain operation

2021 ◽  
Author(s):  
André Hürkamp ◽  
Sebastian Gellrich ◽  
Antal Dér ◽  
Christoph Herrmann ◽  
Klaus Dröder ◽  
...  
Keyword(s):  
Surrogate Modeling ◽  
Temperature History ◽  
Injection Molding Process ◽  
Process Chain ◽  
Molding Process ◽  
Whole Process ◽  
Virtual Production ◽  
Sensor Model ◽  
Fast Prediction ◽  
Operation Phase

AbstractIn this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches.

Improvements in storm surge surrogate modeling for synthetic storm parameterization, node condition classification and implementation to small size databases

2021 ◽  
Author(s):  
Aikaterini P. Kyprioti ◽  
Alexandros A. Taflanidis ◽  
Matthew Plumlee ◽  
Taylor G. Asher ◽  
Elaine Spiller ◽  
...  
Keyword(s):  
Storm Surge ◽  
Surrogate Modeling
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