Anirban Basudhar
University of Arizona
25 Papers
122 Citations
Anirban Basudhar is an academic researcher from University of Arizona. The author has contributed to research in topics: Support vector machine & Probabilistic logic. The author has an hindex of 11, co-authored 25 publications. Previous affiliations of Anirban Basudhar include American Institute of Aeronautics and Astronautics.
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Papers
Adaptive explicit decision functions for probabilistic design and optimization using support vector machines
Anirban Basudhar,Samy Missoum +1 more
TL;DR: This article presents a methodology to generate explicit decision functions using support vector machines (SVM) and proposes an adaptive sampling scheme that updates the decision function.
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Limit state function identification using Support Vector Machines for discontinuous responses and disjoint failure domains
TL;DR: In this article, a method for the explicit construction of limit state functions using Support Vector Machines (SVM) is presented to handle the difficulties associated with the reliability assessment of problems exhibiting discontinuous responses and disjoint failure domains.
156
An improved adaptive sampling scheme for the construction of explicit boundaries
Anirban Basudhar,Samy Missoum +1 more
TL;DR: An improved adaptive sampling scheme for the construction of explicit decision functions (constraints or limit state functions) using Support Vector Machines (SVMs) using substantial modifications to an earlier version of the scheme.
134
A Sampling-Based Approach for Probabilistic Design with Random Fields
Anirban Basudhar,Samy Missoum +1 more
TL;DR: An original technique to incorporate random fields non-intrusively in probabilistic design is presented, based on the extraction of the main features of a random field using a limited number of experimental observations, which allows for an efficient assessment of the probabilities of failure.
51
A sampling-based approach for probabilistic design with random fields
Anirban Basudhar,Samy Missoum +1 more
- 01 Jan 2008
TL;DR: In this article, a technique to incorporate random fields non-intrusively in probabilistic design is presented, based on the extraction of the main features of a random field using a limited number of experimental observations (snapshots).
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