Top Tweets for #sciML
Inverted-U Capacity Trend: Overparameterization actually degraded physics compliance, as the widest network introduced localized violations standard losses missed.
#SciML #PINN #PhysicsAI #DeepLearning #Simulation

New preprint! ๐
Policy-DRIFT combines generative modeling + DRL for turbulence control!!
https://t.co/WfCdq0kZVU
โข 48.95% drag reduction
โข ~16% higher than DRL
โข ~37ร lower actuation energy
Preprint: [add link]
#AI #Turbulence #FluidMechanics #SciML
![ricardovinuesa's tweet photo. New preprint! ๐
Policy-DRIFT combines generative modeling + DRL for turbulence control!!
https://t.co/WfCdq0kZVU
โข 48.95% drag reduction
โข ~16% higher than DRL
โข ~37ร lower actuation energy
Preprint: [add link]
#AI #Turbulence #FluidMechanics #SciML https://t.co/lmmaOlYMfk](https://pbs.twimg.com/media/HI2EYH5WQAALvd2.jpg)
Join us for the live launch of Dyad 3.0, the next evolution in model-based engineering powered by Scientific AI. Get a first look at new capabilities, see live demos, and explore how #agentic #AI is reshaping #engineering workflowsโfrom faster model creation to more efficient #simulation and decision-makingโalongside voices from organisations already building with Dyad, including Synopsys Inc, Mitsubishi Electric Research Laboratories, Binnies, and Boeing. Register now: https://t.co/UKXfGIwbCA
#Julialang #Dyad #ScientificAI #ModelBasedEngineering #AgenticAI #DigitalTwins
Is your PINN actually learning physics, or just "cheating" to fit the data? ๐ต๏ธโโ๏ธ
Here is the first Model Verification & Validation (V&V) example using Moju. Stop guessing and start auditing your AI's physical admissibility.
pip install moju
#SciML #PhysicsAI #PINNs #DigitalTwin

Built on #julialang #sciml, you get all of the autodiff and machine learning integration as well... that will come in the next demo.
Gradient-based optimization struggles when dynamics are not smooth. We introduce DePCoN (Deep PredictorโCorrector Networks) for robust parameter estimation in dynamical systems with discontinuous inputs.
https://t.co/MHSN1ChnxO
#MachineLearning #Optimization #SciML #NeuralODE
๐๐จ๐ฃ๐ฎ is not "just another PINN library."
๐๐ญ ๐ข๐ฌ ๐ญ๐ก๐ ๐๐ข๐ซ๐ฌ๐ญ ๐๐๐๐ข๐๐๐ญ๐๐ ๐ฏ๐๐ซ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐ข๐ง๐ ๐๐จ๐ซ ๐๐ก๐ฒ๐ฌ๐ข๐๐ฌ-๐๐
๐ ๐๐ซ๐ฒ ๐ข๐ญ ๐ง๐จ๐ฐ: pip install moju
๐ป ๐๐ฑ๐ฉ๐ฅ๐จ๐ซ๐ ๐ญ๐ก๐ ๐๐ง๐ ๐ข๐ง๐: https://t.co/M3cJGTi3UK
#SciML #PhysicsAI

๐ Get started: pip install moju
๐ป https://t.co/M3cJGTi3UK
#SciML #CFD #PhysicsAI #JAX #PINN #OpenSource
The 2D Burgers equation models fluid flow where two forces compete:
Wave steepening that sharpens gradients
Viscous smoothing that flattens them
I built a PINN to capture this. At each point (x y) the network predicts horizontal velocity (u) and vertical velocity (v)
#SciML
Join us for a Dyad Modeling Livestream today - this time at 1pm ET / 10 am PT!ย ย Michael Tiller will joining us today to model a hybrid-EV powertrain!
Tune in on YouTube and send us your thoughts in the chat!
https://t.co/YYWUjxzdLG
#dyad #sciml #julialang
Recent optimizations in SciMLSensitivity.jl are having some pretty good payoffs! Given we just 2.5x'd 2 months ago, this next change of 3.2x is putting us almost an order of magnitude ahead! See the latest autodiff benchmarks https://t.co/kMbUbCLBbt
#sciml #julialang

New fastest explicit non-stiff ODE solver? That's right, we now have something beating the pants off of the high order explicit RK methods! Check out the new symbolic-numeric optimized Taylor methods available in DifferentialEquations.jl!
#julialang #diffeq #sciml

This is a great example of how combining physics, engineering insight, and scientific machine learning can deliver real operational impact in complex infrastructure systems.
#JuliaLang #ScientificMachineLearning #SciML #DigitalTwins #PredictiveMaintenance #AssetHealth #WaterIndustry #JuliaHub
https://t.co/PDgUHm5unb
Machine Design spotlights how Scientific Machine Learning is redefining #predictive #maintenance. By combining physics with data, #SciML delivers accurate, scalable insightsโeven with limited telemetryโhelping manufacturers cut downtime and improve reliability at scale. https://t.co/PNnQlD7sIj
#julialang #SciML #PredictiveMaintenance #DigitalTwins #Manufacturing #IndustrialAI #ScientificComputing #JuliaHub #EngineeringInnovation #ReliabilityEngineering
Your college professor teaches you "A-stable methods are required for stiff ODEs". But PSA, the most commonly used stiff ODE solvers (adaptive order BDF methods) are not A-stable. #sciml #numericalanalysis #diffeq #julialang
Physics-Informed Neural Surrogates for Mesh-Invariant Modeling of High-Speed Flows at #AIAA #SciTech!
We built a neural surrogate that predicts aerodynamic behavior 595x faster than CFD while maintaining ~1% relative error.
#sciml #Julialang #CFD #Hypersonics #AIAASciTech

Higher order methods are usually more efficient, but are there methods for which Euler's method is more efficient? Yes there is! It comes right out of the derivation. #julialang #ode #sciml
Scientific machine learning (#SciML) is not just about adding scientific information to machine learning, but it's also about making machine learning accessible to heterogeneous data.
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