Check out our new paper in WRR: in the Prairie Pothole Region, millions of wetlands fill, spill, and connect, making runoff hard to predict. We show how physics + AI can improve prediction of streamflow and wetland storage in ungauged watersheds. #Wetlands #NewPaper #AGUPubs #PhysicsInformedML
How can AI improve vaccine distribution? New research uses physics-informed neural networks to estimate disease parameters & calculate optimal distribution for diverse populations—even with noisy data.
dl.begellhouse.com/journals/558...
#PhysicsInformedML #VaccineDistribution
How to predict population dynamics with incomplete data? fPINNs provide a robust framework for fractional prey-predator models, accurately inferring predator populations from prey data alone under real-world constraints.
dl.begellhouse.com/journals/558...
#PhysicsInformedML #PopulationDynamics
64 citations: Multifidelity transfer learning trains deep CNNs using mostly cheap low-resolution simulations supplemented with select high-res runs—delivering expensive method accuracy at a fraction of the cost.
📖 www.dl.begellhouse.com/journals/558...
#PhysicsInformedML #DataScience
66 citations: Tensor basis GP models for hyperelastic materials build physical invariances into model structure, achieving better predictions with less data than black-box ML approaches.
📖 www.dl.begellhouse.com/journals/558...
#PhysicsInformedML #Materials
Beyond metric preservation: build symplectic structural invariance into representation.
arxiv.org/abs/2512.19409
#ReservoirComputing #RepresentationLearning #InformationGeometry #SymplecticGeometry #HamiltonianDynamics #GeometricDeepLearning #DynamicalSystems #PhysicsInformedML
Solving the Cosmological Vlasov-Poisson Equations with Physics-Informed Kolmogorov-Arnold Networks
Ashutosh Mishra, Emma Tolley et al.
Paper
Details
#Cosmology #PhysicsInformedML #KarnovArnoldNetworks
Physics‑Informed Learning Machine Improves Soil‑Pile Modeling
A physics‑informed learning machine using an extreme learning network trains in about one second, enabling real‑time monitoring of soil‑pile deflection. getnews.me/physics-informed-learnin... #physicsinformedml #geotechnical
Multi-Gradient Descent Boosts Physics-Informed Traffic Flow Models
Researchers introduced training that separates data‑driven and physics losses, using Traditional Multi‑Gradient Descent to improve traffic flow simulations. Read more: getnews.me/multi-gradient-descent-b... #physicsinformedml #trafficflow
Understanding Generalization in Physics‑Informed Machine Learning Models
Researchers find that affine‑variety dimension—not model size—drives generalization in physics‑informed ML; linear and nonlinear PDE tests confirm the results. Read more: getnews.me/understanding-generaliza... #physicsinformedml #generalization
🕸️ Why AI's Next Breakthrough Isn't About More Data: #MKAN
www.buzzsprout.com/2405788/epis...
helioxpodcast.substack.com/publish/post...
#AI #MachineLearning #DataScience #ScientificComputing #TechInnovation #ComputationalScience #PredictiveModeling #DeepLearning #PhysicsInformedML
Hybrid PDE-Deep Neural Network Model for Calcium Dynamics in Neurons
www.dl.begellhouse.com/journals/558...
#NeuralModeling #PhysicsInformedML #PDEs #DeepLearningInScience
Hybrid PDE-Deep Neural Network Model for Calcium Dynamics in Neurons
www.dl.begellhouse.com/journals/558...
#NeuralModeling #PhysicsInformedML #PDEs #DeepLearningInScience
Hybrid PDE-Deep Neural Network Model for Calcium Dynamics in Neurons
www.dl.begellhouse.com/journals/558...
#NeuralModeling #PhysicsInformedML #PDEs #DeepLearningInScience
From #PhysicsInformedML to #MLInformedPhysics: We're excited about the "knowledge discovery" component of this project utilizing vast amount of Earth data. 🌎
There is much more out there to be discovered, as nature's imagination is far greater than that of humans. Don't stop searching.💡
