Optimal estimation (Remote sensing 🛰️)

In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem. It is used very commonly in the geosciences, particularly for atmospheric sounding. A matrix inverse problem looks like this: A x → = y → {\displaystyle \mathbf {A} {\vec {x}}={\vec {y}}} The essential concept is to transform the matrix, A, into a c...

https://en.wikipedia.org/wiki/Optimal_estimation

#OptimalEstimation #RemoteSensing #InverseProblems

Optimal estimation - Wikipedia

Did you know a CT scan uses math to create images from X-ray data? 🤔 Prof. Martin Burger and Samira Kabri from our Research Unit at @DESYnews shared how #inverseproblems turn data into images during "Wir wollen’s wissen" at Hamburg schools. Inspiring future scientists! 💡

@unihh #science #STEMEducation #ScienceOutreach

Morgen zu Gast um 16:15 im Sitzungszimmer des Mathematischen Instituts für das #RTG2491Kolloquium :
Angkana Rüland von der Universität Bonn mit
"On (In-)Stability Mechanisms in Inverse Problems"

#InverseProblems

🔊 Join us for the "#DeepLearning in #InverseProblems" Workshop on 23-24/9/24 at @DESYnews!

Explore the latest in learning-based methods for inverse problems with top experts.

Register by 15/9 👉 http://indico.desy.de/event/45763/

Don't miss out on this opportunity to expand your skills!

AI Lecture on 11 March 2024, 18:15—19:45 with Prof. Dr. Jong Chul Ye from KAIST, exploring advancements in solving inverse problems with diffusion models, including 3D extensions and guidance by text prompts. Location: Theresienstraße 39, Room B 006. Open to the public. #AI #DiffusionModels #InverseProblems
https://www.ai-news.lmu.de/guestlecture/
Guest Lecture

News from the AI communtity at LMU Munich

Today @JulianTachella, Matthieu Terris, Dongdon Chen, and Samuel Hurault gave an introduction to Deepinverse library at #DIPOpt workshop.
#inverseproblems #ComputationalImaging #deeplearning
Highlights of poster presentation session, second day of #DIPOpt workshop.
1) Continuous Lippmann-Schwinger Intensity Diffraction Tomography, by Olivier Leblanc, @kmlv, and @lowrankjack.
2) Deepinverse Python Library, by Julian Tachella, Dongdong Chen, Samuel Hurault and Matthieu Terris.
#inverseproblems, #computationalimaging
The last talk of the second day of #DIPOpt, by Remi Grinonval, “Rapture of the deep: highs and lows of sparsity in a world of depths”.
#Sparsity #inverseproblems
Next speaker is Mike Davies talking about “Unsupervised Machine Imaging: when is data driven knowledge discovery really possible?”
#DIPOpt #inverseproblems #machinelearning #ComputationalImaging #CompressedSensing