home.social

#cfd — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #cfd, aggregated by home.social.

  1. AI-Based Weather Forecasting Has Blind Spots

    Traditional weather forecasting models are physics-based and rely on supercomputers. Practically speaking, this means that they start from the basic governing equations (like the Navier-Stokes equations) and use approximations to model aspects of the problem in order to make the physics solvable, given constraints on time, computational power, spatial resolution, and so on.

    So-called AI models approach the problem differently, training a model on past weather conditions in order to predict future weather. In some respects, this approach is very successful; AI-based models require less computational infrastructure to run and, in recent years, have greatly improved their predictions of everyday weather.

    However, these AI models do poorly when predicting extreme weather events, because their training data contain relatively few examples of these events. They show limited ability to extrapolate their predictions to more extreme events. But these events–like the unprecedented 2021 heatwave in the Pacific Northwest or many of the Category 5 hurricanes we’ve seen in the last decade–are happening increasingly often due to climate change. Those events will keep happening, more frequently, as warming continues. Physics-based models can predict and forecast these events in ways that AI-based models fail to because they are limited by their trained experiences.

    Researchers are working to find ways to better equip AI-based models with more physical sense, but, as these models proliferate, it’s important for their users (and those of us using their forecasts) to know what their current weaknesses are. (Image credit: B. McGowan; research credit: Y. Sun et al.; see also S. Nath and T. Palmer; via Gizmodo)

    #CFD #computationalFluidDynamics #fluidDynamics #hurricane #hurricanes #meteorology #physics #science #weather
  2. 10-minute Video: Top ten tips for understanding post-exertional malaise by Kate Herbert, Nurse Educator at Emerge Australia:

    vimeo.com/1191768719/03bf0f4425

    #PEM #MEcfs #PwME #CFD #LongCovid @mecfs @longcovid

  3. 10-minute Video: Top ten tips for understanding post-exertional malaise by Kate Herbert, Nurse Educator at Emerge Australia:

    vimeo.com/1191768719/03bf0f4425

    #PEM #MEcfs #PwME #CFD #LongCovid @mecfs @longcovid

  4. 10-minute Video: Top ten tips for understanding post-exertional malaise by Kate Herbert, Nurse Educator at Emerge Australia:

    vimeo.com/1191768719/03bf0f4425

    #PEM #MEcfs #PwME #CFD #LongCovid @mecfs @longcovid

  5. 10-minute Video: Top ten tips for understanding post-exertional malaise by Kate Herbert, Nurse Educator at Emerge Australia:

    vimeo.com/1191768719/03bf0f4425

    #PEM #MEcfs #PwME #CFD #LongCovid @mecfs @longcovid

  6. 10-minute Video: Top ten tips for understanding post-exertional malaise by Kate Herbert, Nurse Educator at Emerge Australia:

    vimeo.com/1191768719/03bf0f4425

    #PEM #MEcfs #PwME #CFD #LongCovid @mecfs @longcovid

  7. europesays.com/people/85299/ [TMGM Financial Breakfast] Why Did AMD CEO Lisa Su Return to Shanghai for the First Time in Three Years? #CFD #Forex #LisaSu #TMGM #Trading

  8. europesays.com/people/84977/ [TMGM Financial Breakfast] Why Did AMD CEO Lisa Su Return to Shanghai for the First Time in Three Years? #CFD #Forex #LisaSu #TMGM #Trading

  9. europesays.com/people/81689/ [TMGM Financial Breakfast] Why Did AMD CEO Lisa Su Return to Shanghai for the First Time in Three Years? #CFD #Forex #LisaSu #TMGM #Trading

  10. europesays.com/people/80981/ [TMGM Financial Breakfast] Why Did AMD CEO Lisa Su Return to Shanghai for the First Time in Three Years? #CFD #Forex #LisaSu #TMGM #Trading

  11. Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

    #biology #CFD #computationalFluidDynamics #fluidDynamics #numericalSimulation #physics #pollen #science #trees
  12. Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

    #biology #CFD #computationalFluidDynamics #fluidDynamics #numericalSimulation #physics #pollen #science #trees
  13. Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

    #biology #CFD #computationalFluidDynamics #fluidDynamics #numericalSimulation #physics #pollen #science #trees
  14. Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

    #biology #CFD #computationalFluidDynamics #fluidDynamics #numericalSimulation #physics #pollen #science #trees
  15. Understanding Pollen Dispersal

    When the wind blows, trees shift and sway, reconfiguring their shape and their leaves in response. For parts of the year, that flow can also pluck pollen grains off the tree, carrying them on the winds. A new computational simulation models this pollen dispersal from a tree, with the aim of eventually integrating into a tool for urban planners.

    Trees are an important component to fighting climate change, especially in cities, because they cool their surroundings in addition to providing fresh oxygen. But urban planners recognize the downsides to trees, too–allergies, anyone?–and, with the right tools, they could maximize the trees’ advantages while minimizing pollen spread for allergy-sufferers. (Image credit: M. Köles; research credit: T. Dbouk et al.; via Physics World)

    #biology #CFD #computationalFluidDynamics #fluidDynamics #numericalSimulation #physics #pollen #science #trees
  16. Euro softens against British Pound ahead of Eurozone GDP data

    Euro softens against British Pound ahead of Eurozone GDP data POPULAR ARTICLES The EUR/GBP cross loses momentum to…
    #Europe #EU #EuroZone #CfD #EuroArea #Eurozone #Forex #TMGM #trading
    europesays.com/europe/48708/

  17. Airbus triples compute power with new supercomputer for aircraft design

    French aircraft giant Airbus recently inaugurated new supercomputing infrastructure spread across two sites, one in France and one…
    #Netherlands #Nederland #NL #Europe #Europa #EU #Airbus #Aerospace #AMD #Bull #CFD #digitaltwins #germany #HPE #ibm #InventionsandMachines #Supercomputers
    europesays.com/netherlands/143

  18. europesays.com/people/78659/ [TMGM Financial Breakfast] Why Did AMD CEO Lisa Su Return to Shanghai for the First Time in Three Years? #CFD #Forex #LisaSu #TMGM #Trading

  19. 📣 Registration is open for the Faculty Development Program on CFD using OpenFOAM by FOSSEE, IIT Bombay.

    This free online program is specially designed for faculty members using CFD in research and teaching.

    📅 2–5 June 2026
    💻 Online | Free of cost

    🔗 Register: shorturl.at/CYUzi

    📲 Scan QR code in poster for registration.

    #CFD #OpenFOAM #ComputationalFluidDynamics #EngineeringFaculty #FDP #FOSSEE #IITBombay #OpenSource #Research #EngineeringEducation #Simulation #OpenSource

  20. #FluidX3D #CFD v3.7 brings faster Q-criterion isosurface rendering with #OpenCL local memory optimization! 🖖🤠
    github.com/ProjectPhysX/FluidX

    Instead of 32 velocities for each #GPU thread, now an 8x8x8 workgroup loads & reuses 11x11x11 velocities in L1$, a 12x VRAM BW reduction.

    Fascinating insight: Which thread loads which cell from VRAM to L1$, and which thread renders which grid cell within the workgroup, can be very different!
    github.com/ProjectPhysX/FluidX

    PS: plugged X-wing Gif in #GitHub preview 🖖😜

  21. #FluidX3D #CFD v3.7 brings faster Q-criterion isosurface rendering with #OpenCL local memory optimization! 🖖🤠
    github.com/ProjectPhysX/FluidX

    Instead of 32 velocities for each #GPU thread, now an 8x8x8 workgroup loads & reuses 11x11x11 velocities in L1$, a 12x VRAM BW reduction.

    Fascinating insight: Which thread loads which cell from VRAM to L1$, and which thread renders which grid cell within the workgroup, can be very different!
    github.com/ProjectPhysX/FluidX

    PS: plugged X-wing Gif in #GitHub preview 🖖😜

  22. #FluidX3D #CFD v3.7 brings faster Q-criterion isosurface rendering with #OpenCL local memory optimization! 🖖🤠
    github.com/ProjectPhysX/FluidX

    Instead of 32 velocities for each #GPU thread, now an 8x8x8 workgroup loads & reuses 11x11x11 velocities in L1$, a 12x VRAM BW reduction.

    Fascinating insight: Which thread loads which cell from VRAM to L1$, and which thread renders which grid cell within the workgroup, can be very different!
    github.com/ProjectPhysX/FluidX

    PS: plugged X-wing Gif in #GitHub preview 🖖😜

  23. #FluidX3D #CFD v3.7 brings faster Q-criterion isosurface rendering with #OpenCL local memory optimization! 🖖🤠
    github.com/ProjectPhysX/FluidX

    Instead of 32 velocities for each #GPU thread, now an 8x8x8 workgroup loads & reuses 11x11x11 velocities in L1$, a 12x VRAM BW reduction.

    Fascinating insight: Which thread loads which cell from VRAM to L1$, and which thread renders which grid cell within the workgroup, can be very different!
    github.com/ProjectPhysX/FluidX

    PS: plugged X-wing Gif in #GitHub preview 🖖😜

  24. #FluidX3D #CFD v3.7 brings faster Q-criterion isosurface rendering with #OpenCL local memory optimization! 🖖🤠
    github.com/ProjectPhysX/FluidX

    Instead of 32 velocities for each #GPU thread, now an 8x8x8 workgroup loads & reuses 11x11x11 velocities in L1$, a 12x VRAM BW reduction.

    Fascinating insight: Which thread loads which cell from VRAM to L1$, and which thread renders which grid cell within the workgroup, can be very different!
    github.com/ProjectPhysX/FluidX

    PS: plugged X-wing Gif in #GitHub preview 🖖😜