Skip to main content

Geordie Richards

Profile Picture

Mechanical and Aerospace Engineering

Faculty

Assistant Professor

Contact Information

Go toOffice Location: ENGR 419C
DialPhone: 435-797-6364
SendEmail: geordie.richards@usu.edu


Educational Background

PhD, Mathematics, University of Toronto, 2012
Maximal-in-time behavior of deterministic and stochastic dispersive PDEs
MS, Mathematics, University of Toronto, 2007
BS, Mathematics, University of Toronto, 2006

Research Interests

Dr. Richards conducts research in the analysis of deterministic and stochastic nonlinear partial differential equations (PDEs) from physics and engineering, specifically equations modeling fluids and dispersive phenomena. Dr. Richards also conducts applied research in estimation theory and uncertainty quantification.

Awards

MAE Teacher of the Year, 2019

MAE USU

Research in Groups Grant, 2016

Banff International Research Station, Canada

MSRI Research Membership, 2015

Mathematical Sciences Research Institute

Research in Peace Grant, 2015

Mathematical Research Institute of Oberwolfach, Germany

Research in Pairs Grant, 2014

Mittag-Leffler Institute, Sweden

IMA Postdoctoral Research Fellowship, 2012

Institute for Mathematics and its Applications

Publications - Abstracts

    Publications - Books & Book Chapters

      * Has not been peer reviewed

      Publications - Fact Sheets

        * Has not been peer reviewed

        Publications - Curriculum

          * Has not been peer reviewed

          Publications - Journal Articles

            Academic Journal

          • Smith, B.L, Neal, D.R, Feero, M., Richards, G., (2018). Assessing the Limitations of Effective Number of Samples for Finding the Uncertainty of the Mean of Correlated Data. Measurement Science and Technology, 29:12, 125304.
          • Friedlander, S., Foldes, J., Glatt-Holtz, N., Richards, G., (2017). Asymptotic analysis for randomly forced MHD. SIAM Journal on Mathematical Analysis, 1-25.
          • Glatt-Holtz, N., Mattingly, J., Richards, G., (2017). On unique ergodicity in nonlinear stochastic partial differential equations. Journal of Statistical Physics, 1-24.
          • Foldes, J., Glatt-Holtz, N., Richards, G., Whitehead, J., (2016). Ergodicity in randomly forced Rayleigh-Benard convection. Nonlinearity, 29:11, 3309-3346.
          • Oh, T., Richards, G., Thomann, L., (2016). On invariant Gibbs measures for the generalized KdV equations. Dynamics of Partial Differential Equations, 13:2, 133-153.
          • Richards, G., (2016). Invariance of the Gibbs measure for the periodic quartic gKdV. Annales de l'Institut Henri Poincare (C) Non Linear Analysis, 33:3, 699-766.
          • Foldes, J., Glatt-Holtz, N., Richards, G., Thomann, E., (2015). Ergodic and mixing properties of the Boussinesq equations with a degenerate random forcing. Journal of Functional Analysis, 269:8, 2427-2504.
          • Mueller, C., Richards, G., (2014). Can solutions of the wave equation with nonlinear multiplicative noise blow-up?. Open Problems in Mathematics
          • Richards, G., (2014). Well-posedness of the stochastic KdV-Burgers equation. Stochastic Processes and their Applications, 124:4, 1627-1647.
          • Richards, G., (2011). Mass Concentration for the Davey-Stewartson system. Differential and Integral Equations, 24:3-4, 261-280.

          * Has not been peer reviewed

          Publications - Literary Journal

            * Has not been peer reviewed

            Publications - MultiMedia

              * Has not been peer reviewed

              Publications - Technical Reports

                * Has not been peer reviewed

                Publications - Translations & Transcripts

                  Publications - Other

                    * Has not been peer reviewed

                    Scheduled Teaching

                    MAE 3210 - Engineering Numerical Methods, Spring 2019

                    MAE 3210 - Engineering Numerical Methods, Spring 2019

                    MAE 3210 - Engineering Numerical Methods, Spring 2019

                    MAE 3210 - Engineering Numerical Methods, Spring 2019

                    MAE 3210 - Engineering Numerical Methods, Spring 2019

                    MAE 3210 - Engineering Numerical Methods, Spring 2019

                    MAE 6040 - Continuum Mechanics and Elasticity, Fall 2018

                    MAE 3210 - Engineering Numerical Methods, Spring 2018

                    MAE 6500 - Potential Flow, Fall 2017

                    MAE 6490 - Turbulence, Spring 2017

                    MAE 6500 - Potential Flow, Fall 2016


                    Graduate Students Mentored

                    Jacob Bryan, Mechanical & Aerospace Engineering, May 2019
                    Louis Tonc, Mechanical & Aerospace Engineering, December 2016
                    Joe James, Mechanical & Aerospace Engineering, September 2016 - May 2019