Reduced-order models at work: Industry and Medicine

Contribution List

29. Some new developments on the non invasive reduced basis method

Prof. Yvon Maday (UPMC Paris 6)

3/30/22, 10:30 AM

The efficient implementation of reduced basis methods relying on a high fidelity discretization method to compute the elements of the reduced basis, requires to enter within the code, offline, so that the online solution can be produced very rapidly. Since this is sometimes impossible, in particular for codes used in industrial framework, we have proposed a Non Invasive alternative where the...

28. Hemodynamics examples of reduced order models for clinical applications

Prof. Irène Vignon-Clémentel (Inria Saclay)

3/30/22, 11:20 AM

30. ROM Closures and Stabilizations for Under-Resolved Turbulent Flows

Prof. Traian Iliescu (Virginia Tech)

3/30/22, 1:40 PM

In this talk, I will survey reduced order model (ROM) closures and
stabilizations for under-resolved turbulent flows. Over the past
decade, several closure and stabilization strategies have been
developed to tackle the ROM inaccuracy in the convection-dominated,
under-resolved regime, i.e., when the number of degrees of freedom is
too small to capture the complex underlying dynamics. I...

31. Field reconstruction using manifold learning and structure-preserving metrics

Dr Lionel Mathelin (LIMSI-CNRS)

3/30/22, 2:30 PM

The problem of estimating the state of a physical system is ubiquitous in science. However observations are always limited so that the high-dimensional state cannot be observed and the associated mathematical problem is ill-posed. Popular workarounds include dimension reduction and regularization by imposing some structure to the class of elements in which the estimation is sought.
We here...

32. Use of reduced basis techniques for two-phase flows in porous media

Dr Guillaume Enchery (IFPEN)

3/30/22, 3:20 PM

33. Quadratic Approximation Manifold for Mitigating the Kolmogorov Barrier in Nonlinear Projection-Based Model Order Reduction

Prof. Charbel Farhat (Stanford)

3/31/22, 9:00 AM

A quadratic approximation manifold is presented for performing nonlinear, projection-based, model order reduction (PMOR). It constitutes a departure from the traditional affine subspace approximation aimed at mitigating the Kolmogorov barrier for nonlinear PMOR, particularly for convection-dominated transport problems. It builds on the data-driven approach underlying the traditional...

34. Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking

Prof. Mattew Zahr (Notre Dame)

3/31/22, 9:50 AM

Partial differential equations (PDEs) that model convection-dominated phenomena often arise in engineering practice and scientific applications, ranging from the study of high-speed, turbulent flow over vehicles to wave propagation through solid media. The solutions of these equations are characterized by local features or disturbances that propagate throughout the domain as time evolves or a...

35. From handcrafted Galerkin models to automated cluster models

Prof. Bernd Noack (Harbin Institute ot Technology, Shenzhen, China)

3/31/22, 11:10 AM

Reduced-order models (ROM) are of paramount importance for physical understanding, data
compression, estimation, control and optimization. Over a century ago, simple dynamical models
of coherent structures have been facilitated by stability theory (Orr-Sommerfeld equation
1907) and by vortex models (von K arm an 1911). Data-driven reduced-order modeling of coherent
structures has been...

36. Reduced basis Smagorinsky turbulence models

Prof. Tomás Chacón (Instituto de Matemáticas de la Universidad de Sevilla)

3/31/22, 12:00 PM

39. Fear and loathing in Aerodynamics: Are ROMs chimeras or game-changers?

Dr Haysam Telib (Optimad)

3/31/22, 2:30 PM

Over the last years promising advances in reduced order modelling and machine learning have been reported by the scientific community.
Within this talk we will try to project (some of) these findings on different industrial settings to discuss their potential.
We will try to make this evaluation using all metrices that are relevant in industry to identify applications where ROMs can have a...

38. TBD

Dr Florian Bernard (Nurea)

3/31/22, 3:00 PM

40. TBD

Dr Guilhem Ferté (EDF)

3/31/22, 3:30 PM

41. Beyond PCA by explicitely taking into account system symmetries

Prof. Denis Sipp (ONERA)

4/1/22, 9:30 AM

Linear principal component analysis (PCA) experiences an increase in the dimensionality of the latent space when it is applied to configurations that exhibit symmetries. In this study, we introduce a novel machine learning embedding, which uses spatial transformer networks and siamese networks to account for continuous and discrete symmetries, respectively. This embedding, which we term...

42. Tensor methods for high-dimensional problems and model reduction

Dr Damiano Lombardi (Inria Paris)

4/1/22, 10:20 AM

We present several contributions related to Tensor methods for high-dimensional problems and discuss how they are inherently related to model reduction.
In particular, we will show several ways to introduce a principle of adaptivity, making tensor representations more suitable to parsimoniously represent certain solutions sets. In the last part of the talk, a contribution on a possible way to...

43. Model order reduction for CFD: state of the art, advances in applications

Prof. Gianlugi Rozza (SISSA)

4/1/22, 11:40 AM

44. POD- and RB-Hierarchical Model Reduction Techniques in a Parametrized Setting

Prof. Simona Perotto (Politecnico Milano)

4/1/22, 12:30 PM

Different methods have been proposed in the scientific panorama to offer a compromise between modeling accuracy and computational efficiency. Reduced order models represent a widespread solution in such a direction. In this presentation, we focus on the Hierarchical Model (HiMod) reduction technique, which proved to be an effective approach to discretize CFD configurations where a principal...

37. TBD

Prof. Umberto Morbiducci (Politecnico di Torino)