A. Alcolea
Environmental Engineering · 2009–2010
A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 24 citations into it; #985 of 5,479 by trust here, #2,998 by citations
- Ocean Engineering — 10 citations into it; #400 of 3,193 by trust here, #2,433 by citations
- Civil and Structural Engineering — 9 citations into it
- Geophysics — 8 citations into it; #281 of 1,958 by trust here, #1,522 by citations
- Mechanical Engineering — 7 citations into it
Whom A. Alcolea trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jef Caers
- Sébastien Strebelle
- André G. Journel
- Vivek S. Borkar
- Jesús Carrera
- T. Hoffman
- Anil K. Jain
- Tatiana Chugunova
- H. H. Franssen
- Roger Fletcher
Who trusts A. Alcolea overall
- Gregoire Mariethoz
- Philippe Renard
- Pejman Tahmasebi
- Haiyan Zhou
- Niklas Linde
- Muhammad Sahimi
- Jef Caers
- J. Jaime Gómez-Hernández
- Liangping Li
- Julien Straubhaar
Papers from A. Alcolea's perspective
Ordered by how strongly the researchers A. Alcolea trusts endorse each paper. Same corpus, a different order for every researcher.
- Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics
- Combining Knowledge from Diverse Sources: An Alternative to Traditional Data Independence Hypotheses
- A modified Hausdorff distance for object matching
- Optimization by Simulated Annealing
- The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling
- Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization
- Multiple-Point Simulations Constrained by Continuous Auxiliary Data
- Comparing the Gradual Deformation with the Probability Perturbation Method for Solving Inverse Problems
- Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data
- A hybrid regularized inversion methodology for highly parameterized environmental models