New interval methodologies for reliable chemical process modeling

By Chao-Yang Gau, Mark A. Stadtherr. 2002

58 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3437d51d11fb2178570abeb903c1415a5818dd10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Chrysanthos E. Gounaris
2. Christodoulos A. Floudas
3. Mark A. Stadtherr
4. Chao-Yang Gau
5. Youdong Lin
6. John L. Klepeis

In Statistical and Nonlinear Physics

1. Christodoulos A. Floudas
2. Chrysanthos E. Gounaris
3. Mark A. Stadtherr
4. Chao-Yang Gau
5. Youdong Lin

In Statistics and Probability

1. Christodoulos A. Floudas
2. Chrysanthos E. Gounaris
3. Chao-Yang Gau
4. Mark A. Stadtherr

Citations in the corpus

• Deterministic global optimization for error-in-variables parameter estimation (2002, methodology): “It should be noted that recent enhancements (Gau and Stadtherr, 2002) to the methodology, involving the formulation and solution of the interval-Newton equation, and including the use of a new preconditioning strategy, play an important role in achieving computational efficiency on the problems…”
• A review of recent advances in global optimization (2009, methodology): “Gau and Stadtherr (2002b) studied synchronous work stealing, synchronous and asynchronous diffusive load balancing on a two-dimensional torus virtual network, developed a distributed computing interval Newton framework, and showed that superlinear speedups can be obtained for vapor–liquid…”