activity
20192025
most citedFourier Spectrum Discrepancies in Deep Network Generated Images

57 citations · 192 across the 23 of their papers we have counts for

collaborators

24 papers

physics.flu-dyn2025

Direct numerical simulations of the supersonic Taylor--Green vortex via the Boltzmann equation

Tarik Dzanic, Will Trojak, Luigi Martinelli

We explore the dynamics of the three-dimensional compressible Taylor--Green vortex from the perspective of kinetic theory by directly solving the six-dimensional Boltzmann equation…

math.NA2025

Second-order invariant-domain preserving approximation to the multi-species Euler equations

Bennett Clayton, Tarik Dzanic, Eric J. Tovar

This work is concerned with constructing a second-order, invariant-domain preserving approximation of the compressible multi-species Euler equations where each species is modeled b…

math.NA2025

A method for bounding high-order finite element functions: Applications to mesh validity and bounds-preserving limiters

Tarik Dzanic, Tzanio Kolev, Ketan Mittal

We introduce a novel method for bounding high-order multi-dimensional polynomials in finite element approximations. The method involves precomputing optimal piecewise-linear boundi…

physics.comp-ph2024

PyFR v2.0.3: Towards Industrial Adoption of Scale-Resolving Simulations

Freddie D. Witherden, Peter E. Vincent, Will Trojak +17

PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accura…

math.NA2024

High-order limiting methods using maximum principle bounds derived from the Boltzmann equation I: Euler equations

Tarik Dzanic, Luigi Martinelli

The use of limiting methods for high-order numerical approximations of hyperbolic conservation laws generally requires defining an admissible region/bounds for the solution. In thi…

math.NA2024

A note on higher-order and nonlinear limiting approaches for continuously bounds-preserving discontinuous Galerkin methods

Tarik Dzanic

In (Dzanic, J. Comp. Phys., 508:113010, 2024), a limiting approach for high-order discontinuous Galerkin schemes was introduced which allowed for imposing constraints on the soluti…

math.NA2024

Continuously bounds-preserving discontinuous Galerkin methods for hyperbolic conservation laws

Tarik Dzanic

For finite element approximations of transport phenomena, it is often necessary to apply a form of limiting to ensure that the discrete solution remains well-behaved and satisfies…

physics.flu-dyn2023

Direct molecular gas dynamics simulations of re-entry vehicles via the Boltzmann equation

Tarik Dzanic, Luigi Martinelli

This work explores the feasibility of performing three-dimensional molecular gas dynamics simulations of hypersonic flows such as re-entry vehicles through directly solving the six…

math.NA2023

DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

Tarik Dzanic, Ketan Mittal, Dohyun Kim +4

We introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost a…

physics.flu-dyn2023★ 1 cited

Positivity-preserving discontinuous spectral element methods for compressible multi-species flows

Will Trojak, Tarik Dzanic

We introduce a novel positivity-preserving, parameter-free numerical stabilisation approach for high-order discontinuous spectral element approximations of compressible multi-speci…

physics.flu-dyn2023★ 1 cited

Towards full molecular gas dynamics simulations of complex flows via the Boltzmann equation

Tarik Dzanic, Luigi Martinelli

This work explores the capability of simulating complex fluid flows by directly solving the Boltzmann equation. Due to the high-dimensionality of the governing equation, the substa…

physics.flu-dyn2023★ 1 cited

Validation of wall boundary conditions for simulating complex fluid flows via the Boltzmann equation: Momentum transport and skin friction

Tarik Dzanic, Freddie D. Witherden, Luigi Martinelli

The influence and validity of wall boundary conditions for non-equilibrium fluid flows described by the Boltzmann equation remains an open problem. The substantial computational co…

math.NA2023★ 1 cited

On the anti-aliasing properties of entropy filtering for discontinuous spectral element approximations of under-resolved turbulent flows

Tarik Dzanic, Will Trojak, Freddie D. Witherden

For large Reynolds number flows, it is typically necessary to perform simulations that are under-resolved with respect to the underlying flow physics. For nodal discontinuous spect…

math.NA2023★ 11 cited

Positivity-preserving entropy filtering for the ideal magnetohydrodynamics equations

Tarik Dzanic, Freddie D. Witherden

In this work, we present a positivity-preserving adaptive filtering approach for discontinuous spectral element approximations of the ideal magnetohydrodynamics equations. This app…

cs.LG2022★ 4 cited

Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement

Jiachen Yang, Ketan Mittal, Tarik Dzanic +5

Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need…

math.NA2022★ 9 cited

A positivity-preserving and conservative high-order flux reconstruction method for the polyatomic Boltzmann--BGK equation

Tarik Dzanic, Freddie D. Witherden, Luigi Martinelli

In this work, we present a positivity-preserving high-order flux reconstruction method for the polyatomic Boltzmann--BGK equation augmented with a discrete velocity model that ensu…

math.NA2022★ 56 cited

Positivity-Preserving Entropy-Based Adaptive Filtering for Discontinuous Spectral Element Methods

Tarik Dzanic, Freddie D. Witherden

In this work, we present a positivity-preserving entropy-based adaptive filtering method for shock capturing in discontinuous spectral element methods. By adapting the filter stren…

math.NA2021

Utilizing Time-Reversibility for Shock Capturing in Nonlinear Hyperbolic Conservation Laws

Tarik Dzanic, Will Trojak, Freddie D. Witherden

In this work, we introduce a novel approach to formulating an artificial viscosity for shock capturing in nonlinear hyperbolic systems by utilizing the property that the solutions…

math.NA2021

Bounds Preserving Temporal Integration Methods for Hyperbolic Conservation Laws

Tarik Dzanic, Will Trojak, Freddie D. Witherden

In this work, we present a modification of explicit Runge-Kutta temporal integration schemes that guarantees the preservation of any locally-defined quasiconvex set of bounds for t…

physics.flu-dyn2021★ 10 cited

Partially-Averaged Navier-Stokes Simulations of Turbulence Within a High-Order Flux Reconstruction Framework

Tarik Dzanic, Sharath Girimaji, Freddie Witherden

High-order methods and hybrid turbulence models have independently shown promise as means of decreasing the computational cost of scale-resolving simulations. The objective of this…

cs.LG2021★ 7 cited

Reinforcement Learning for Adaptive Mesh Refinement

Jiachen Yang, Tarik Dzanic, Brenden Petersen +9

Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate c…

math.NA2020

A Riemann Difference Scheme for Shock Capturing in Discontinuous Finite Element Methods

Tarik Dzanic, Will Trojak, Freddie D. Witherden

We present a novel structure-preserving numerical scheme for discontinuous finite element approximations of nonlinear hyperbolic systems. The method can be understood as a generali…

physics.comp-ph2020★ 34 cited

Accuracy, Stability, and Performance Comparison between the Spectral Difference and Flux Reconstruction Schemes

Christopher Cox, Will Trojak, Tarik Dzanic +2

We report the development of a discontinuous spectral element flow solver that includes the implementation of both spectral difference and flux reconstruction formulations. With th…

eess.IV2019★ 57 cited

Fourier Spectrum Discrepancies in Deep Network Generated Images

Tarik Dzanic, Karan Shah, Freddie Witherden

Advancements in deep generative models such as generative adversarial networks and variational autoencoders have resulted in the ability to generate realistic images that are visua…