About

Clint N. Dawson is a professor and the Department Chair of the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. He holds the Cockrell Family Regents Chair in Engineering #2 and is also the director of the Computational Hydraulics Group in the Oden Institute for Computational Engineering and Sciences. Dawson's research interests encompass numerical methods for partial differential equations, with a focus on flow and transport problems in computational fluid dynamics (CFD), scientific computing, parallel computing, finite element analysis, and discontinuous Galerkin methods. His work also includes shallow water systems, hurricane storm surge modeling, rainfall-induced flooding, groundwater systems, flow in porous media, geochemistry, data assimilation, parameter estimation, and uncertainty and error estimation. Dawson received his Bachelor of Arts and Master of Science degrees in mathematics from Texas Tech University in 1982 and 1984, respectively, and earned his Ph.D. in mathematical sciences from Rice University in 1988. His academic career includes positions at Rice University, where he was promoted to associate professor in 1994, and later at the University of Texas at Austin, where he became a full professor in 2000. He has been recognized with several prestigious titles and awards, including the Edward S. Hyman Endowed Chair in Engineering, the John J. McKetta Centennial Energy Chair in Engineering, and the University of Texas President's Research Impact Award in 2024. Dawson has authored or co-authored over 200 technical articles in his field and has served in leadership roles such as Chair of the Society for Industrial and Applied Mathematics Activity Group on Geosciences. He is currently the managing editor of Computational Geosciences and has received numerous honors, including the SIAM Geosciences Career Prize in 2013 and being named a Fellow of SIAM in 2016.

Research topics

• Machine Learning
• Artificial Intelligence
• Computer Science
• Data Mining
• Geography
• Geology
• Mathematics
• Algorithm
• Climatology
• Statistics
• Environmental science
• Meteorology
• Oceanography

Selected publications

• R2-D2 Smoothing: A novel DEM smoothing method for kinematic wave models

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Taen: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Variational Data-Consistent Assimilation

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• A decade of DesignSafe: enabling open science in natural hazards

  Frontiers in Built Environment · 2025-07-30 · 1 citations

  articleOpen access

  DesignSafe ( www.designsafe-ci.org ) is the leading cyberinfrastructure for engineering and social science research related to natural hazards. It provides tools for managing, analyzing, and sharing data, helping researchers study how natural hazards affect both physical infrastructure and communities. DesignSafe connects curated datasets from academic experimental facilities and field reconnaissance teams to researchers focused on data analysis, computation, and numerical simulation. The platform provides researchers with petabyte-scale storage and hundreds of millions of computing hours mediated by intuitive interfaces that lower the bar of entry to advanced computational capabilities. By enabling sophisticated simulations and data-driven workflows previously unattainable with desktop computers or small clusters/servers and enabling streamlined data curation, publication, and dissemination, DesignSafe empowers researchers to accelerate discoveries and helps them amplify the impact of their work.

  PublisherOA PDFDOI

• A Neural Operator Emulator for Coastal and Riverine Shallow Water Dynamics

  arXiv (Cornell University) · 2025-02-20 · 2 citations

  preprintOpen accessSenior author

  Coastal regions and river floodplains are particularly vulnerable to the impacts of extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas is essential for infrastructure planning and climate adaptation. Yet high-fidelity numerical models are often too computationally expensive for real-time use, and lower-cost approaches, such as traditional model order reduction algorithms or conventional neural networks, typically struggle to generalize to out-of-distribution conditions. In this study, we present the Multiple-Input Temporal Operator Network (MITONet), a novel autoregressive neural emulator that employs latent-space operator learning to efficiently approximate high-dimensional numerical solvers for complex, nonlinear problems that are governed by time-dependent, parameterized partial differential equations. We showcase MITONet's predictive capabilities by forecasting regional tide-driven dynamics in the Shinnecock Inlet in New York and riverine flow in a section of the Red River in Louisiana, both described by the two-dimensional shallow-water equations (2D SWE), while incorporating initial conditions, time-varying boundary conditions, and domain parameters such as the bottom friction coefficient. Despite the distinct flow regimes, the complex geometries and meshes, and the wide range of bottom friction coefficients studied, MITONet displays consistently high predictive skill, with anomaly correlation coefficients above 0.9, a maximum normalized root mean square error of 0.011, and computational speedups between 100x-1,250x, even for 175 days of autoregressive rollout forecast from random initial conditions and with unseen parameter values.

  PublisherOA PDFDOI

• Coupled Continuous-Discontinuous Galerkin Finite Element Solver for Compound Flood Simulations

  arXiv (Cornell University) · 2025-08-21

  preprintOpen accessSenior author

  Several recent tropical cyclones, e.g., Hurricane Harvey (2017), have lead to significant rainfall and resulting runoff. When the runoff interacts with storm surge, the resulting floods can be greatly amplified and lead to effects that cannot be correctly modeled by simple superposition of its distinctive sources. In an effort to develop accurate numerical simulations of runoff, surge, and compounding floods, we develop a locally conservative coupled DG-CG discretization of the shallow water equations and integrate it into the Advanced Circulation Model (ADCIRC). We also modify the continuity equation to include spatially and temporally variable rainfall into the model using parametric rainfall models. We demonstrate the capabilities of the scheme though a sequence of physically relevant numerical tests, including small scale test cases based on laboratory measurements and large scale experiments with Hurricane Harvey in the Gulf of Mexico. The results highlight the conservation properties and robustness of the developed method and show the potential of compound flood modeling using our approach.

  PublisherOA PDFDOI

• Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback

  ArXiv.org · 2025-02-03

  preprintOpen access

  Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.

  PublisherOA PDFDOI

• Variational Data-Consistent Assimilation

  ArXiv.org · 2025-11-03

  preprintOpen accessSenior author

  This work introduces a new class of four-dimensional variational data assimilation (4D-Var) methods grounded in data-consistent inversion (DCI) theory. The methods extend classical 4D-Var by incorporating a predictability-aware regularization term. The first method formulated is referred to as Data-Consistent 4D-Var (DC-4DVar), which is then enhanced using a Weighted Mean Error (WME) quantity-of-interest map to construct the DC-WME 4D-Var method. While the DC and DC-WME cost functions both involve a predictability-aware regularization term, the DC-WME function includes a modification to the model-data misfit, thereby improving estimation accuracy, robustness, and theoretical consistency in nonlinear and partially observed dynamical systems. Proofs are provided that establish the existence and uniqueness of the minimizer and analyze how a predictability assumption that is common within the DCI framework helps to promote solution stability. Numerical experiments are presented on benchmark dynamical systems (Lorenz-63 and Lorenz-96) as well as for the shallow water equations (SWE). In the benchmark dynamical systems, the DC-WME 4D-Var formulation is shown to consistently outperform standard 4D-Var in reducing both error and bias while maintaining robustness under high observation noise and short assimilation windows. Despite introducing modest computational overhead, DC-WME 4D-Var delivers improvements in estimation performance and forecast skill, demonstrating its potential practicality and scalability for high-dimensional data assimilation problems.

  PublisherOA PDFDOI

• Hurri-Gan: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations Using Generative Adversarial Networks

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• GPU-acceleration of the Discontinuous Galerkin Shallow Water Equations Model (DG-SWEM) with OpenACC

  ArXiv.org · 2025-08-28

  preprintOpen accessSenior author

  This paper presents a porting of {DG-SWEM}, a first-order discontinuous Galerkin solver for storm surge based on the Advanced Circulation Model (ADCIRC), to NVIDIA GPUs. Time-explicit discontinuous Galerkin methods contain a large number of degrees of freedom but have been shown to exhibit a large amount of data parallelism due to the loose coupling between elements, and thus are naturally mapped to the GPU architecture. A previous framework in porting DG-SWEM to GPUs required converting subroutines from Fortran to C++ to be used with CUDA C++. By using OpenACC and Unified Memory, we simplify the porting process and maintain a single codebase for both CPU and GPU versions. We test the code using a large Hurricane Harvey scenario on NVIDIA's Grace Hopper chip, and compare the GPU code's performance on multiple H200 nodes to the CPU version on the same amount of Grace CPU nodes.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Numerical and Probabilistic Modeling of Aboveground Storage Tanks Subjected to Multi-Hazard Storm Events

  NSF · $240k · 2016–2020

• PREEVENTS Track 2: Collaborative Research: A Dynamic Unified Framework for Hurricane Storm Surge Analysis and Prediction Spanning across the Coastal Floodplain and Ocean

  NSF · $359k · 2019–2024

• Collaborative Research: Construction and Analysis of Numerical Methods for Stochastic Inverse Problems with Application to Coastal Hydrodynamics

  NSF · $100k · 2018–2022

• SI2-SSI: Collaborative Research: STORM: A Scalable Toolkit for an Open Community Supporting Near Realtime High Resolution Coastal Modeling

  NSF · $540k · 2014–2019

• Collaborative Research: Advancing the Data-to-Distribution Pipeline for Scalable Data-Consistent Inversion to Quantify Uncertainties in Coastal Hazards

  NSF · $175k · 2022–2026

Frequent coauthors

• Eirik Valseth

  The University of Texas at Austin

  68 shared

• Joannes J. Westerink

  University of Notre Dame

  54 shared

• Troy Butler

  39 shared

• Ibrahim Hoteit

  King Abdullah University of Science and Technology

  32 shared

• Mary F. Wheeler

  32 shared

• J. C. Dietrich

  North Carolina State University

  24 shared

• Ethan J. Kubatko

  The Ohio State University

  23 shared

• Jennifer Proft

  The University of Texas at Austin

  22 shared

Labs

• Computational Hydraulics GroupPI

Education

• B.A.

  Texas Tech University

  1982

• M.S.

  Texas Tech University

  1984

• Ph.D., mathematical sciences

  Rice University

  1988

Awards & honors

• Institute for Computational Engineering and Sciences Disting…
• Society for Industrial and Applied Mathematics Geosciences C…
• Fellow of the Society for Industrial and Applied Mathematics…
• University of Texas President's Research Impact Award (2024)