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The CADES team conducts research into foundational theory in computational mathematics and statistics to increase accuracy and efficiency of numerical models. The research impacts applied domains involving wave propagation, optical devices, forecasting of weather, air quality, and drought, templates of disease progression in cancer and dementia, and multifacility location problems for warehouse and wireless services. All projects involve high performance computing and open-source scientific software products.
Publications
Publications
LOCal workshops & conferences
LOCal workshops & conferences
The RAIN Meet is regional gathering of researchers for rapid and informal communication of ongoing research activities in computational and applied mathematics. More details at https://sites.google.com/oregonstate.edu/rain2025
Seminar on Continuum Mechanics
Seminar on Continuum Mechanics
By Dr. Michael Neunteufel
Part 1: Mondays and Wednesdays 2-3pm, March 31 - April 16, 2025.
Part 2: Mondays and Wednesdays 2-3pm, May 12 - May 21, 2025.
Consult Prof. Jeffrey Ovall or the conference website for more details.
The RAIN Meet is regional gathering of researchers for rapid and informal communication of ongoing research activities in computational and applied mathematics. More details at https://sites.google.com/pdx.edu/rain2024
July 9-11, 2023: NGSolve User Meeting in Portland.
July 9-11, 2023: NGSolve User Meeting in Portland.
More details at https://ngsolve.org
November 12, 2022: Northwest Undergraduate Mathematics Symposium (NUMS).
More details at conference website.
September 13, 2022: First PSU workshop on learning nonparametric differential equations from data
September 13, 2022: First PSU workshop on learning nonparametric differential equations from data
Co-sponsored by a Google Research Award and NSF RTG grant DMS-2136228
Background: Consider data arising from a differential equation, ordinary, partial, or stochastic. This data is univariate, multivariate, or functional and observed with noise at a small number of time points. How to learn these differential equations? Are they even learnable? If yes, what guarantee can be provided? We are interested in the nonparametric case when there is no known analytical form for the differential equations.
When: Tuesday, September 13th, 9 am to 5 pm.
Where: Portland State University, FMH-4-462 MTH LargeConferenceRoom
Program: Includes talk by PSU faculty and students, Kamel Lahouel (TGen), and round table discussion with the attendees, including Dr. Ehsan Variani, from Google Inc.
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