Model simplification by asymptotic order of magnitude reasoning

Authors:
Kenneth Man-kam Yip
Affiliations:
Department of Computer Science, Yale University, New Haven, CT
Venue:
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Year:
1993

Citing 6
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Exaggeration

Artificial Intelligence
Order of magnitude reasoning

Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Compositional modeling: finding the right model for the job

Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Graphs of models

Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Reasoning about model accuracy

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A new method for proving certain Presburger formulas

IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1

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Abstract

One of the hardest problems in reasoning about a physical system is finding an approximate model that is mathematically tractable and yet captures the essence of the problem. Approximate models in science are often constructed by informal reasoning based on consideration of limiting cases, knowledge of relative importance of terms in the model, and understanding of gross features of the solution. We show how an implemented program can combine such knowledge with a heuristic simplification procedure and an inequality reasoner to simplify difficult fluid equations.