Top Tweets for #inverseProblems
We start the 3rd day of #XQCD2026 @pknu_official with a plenary by Kai Zhou (@cuhksz) reviewing recent progress in machine learning for extreme nuclear matter. Key focus: #inverseproblems, extracting physics insight from experimental or simulation data. Bayesian inference & AD.
I’m pleased to share that our article has just been published with @SpringerNature. https://t.co/MPTdxgdRJY #Research #Tomography #Optimization #InverseProblems #Imaging #NondestructiveEvaluation
Grateful to have presented my solo-authored research at NeurIPS 2025 through the Machine Learning and the Physical Sciences (ML4PS) workshop at age 16.
#NeurIPS2025 #ML4PS #MachineLearning #Physics #InverseProblems #StudentResearcher #AIforScience
Thank you, @CUSEAS, for publishing the story "Understanding How Materials Behave by Watching Them Move" based on our paper in PNAS!
#DIC #WARP #Differentialsimulation #inverseproblems #AI4Science
https://t.co/Lyl2DHScYU
It's day 2 of #SIAMAN25 in Montreal! Emilie Chouzenoux of @centralesupelec and @UnivParisSaclay is currently speaking about the deep unfolding approach for #inverseproblems in imaging and the use case of limited-angle #computedtomography. Check it out!
🎉 Thrilled to share our latest work on solving inverse problems via diffusion-based priors — without heuristic approximations of the measurement matching score!
📄 Link: https://t.co/FAXh6HIt2F (1/6)
#DiffusionModels #InverseProblems #Guidance #SequentialMonteCarlo
https://t.co/lOBgjrhze4
Inverse Problems for Integro-differential Operators by Lin & Liu (Applied Mathematical Sciences, Vol. 222)
A deep dive into theory & computation of inverse problems in applied math.
📘https://t.co/lME8KNKfid
#Math #SpringerMath #InverseProblems #NewBook
After 55 years, Baarda’s geodetic reliability theory (1968) is FINALLY challenged🚨New study unveils a superior regularized external reliability measure—up to 70% more accurate for ill-posed problems.🌍 #Tech #DataScience #InverseProblems #STEM
Read more: https://t.co/DiayWO7z3q
🧪 A key challenge to solving #InverseProblems, especially in science and engineering settings, is limited data and compute for simulations.
🥧 The @iclr_conf work of @apivich_h @greglau , PIED, is an #ExperimentalDesign framework for #PDE inverse problems that efficiently optimizes for the best observation inputs.
📌 PIED utilizes #PINNs to perform forward simulation and to solve the inverse problems, which allows for higher efficiency over numerical simulators and allow for meshless and differentiable simulations.
🤖 Using PINNs along with differentiable observation selection criteria, PIED selects the optimal design parameters for one-shot deployment, while exploitation parallel computation and gradient-based optimization methods.
📈 These benefits allow PIED to outperform existing ED benchmarks in IPs for both finite-dimensional and function-values inverse parameters, as demonstrated in various settings including on real experimental data.
Check out our #ICLR2025 Poster #28 on 26 Apr in Poster Session 6 Sat 3pm Hall 3 + Hall 2B.
Paper: https://t.co/9IMhSEpnp9
GitHub: https://t.co/Qa4ivyY4i0
The field of #InverseProblems exhibits strong connections between pure #research and practical applications. In SIAM News, @SamuliSiltanen details the Finnish inverse problems community's serious investment in this aspect over recent decades. Learn more: https://t.co/Fz2hkI8Sqz
New paper on #machinelearning for #inverseproblems: we show that sampling methods for inclusion detection are neural networks, and can be learned!
Joint work with Shiwei Sun
@malga_center, @UniGenova
Tomorrow, November 13, at 5:30pm CET, I'll give a talk on sampling in #inverseproblems, for the Harmonic Analysis E-Seminars
https://t.co/HW9P28g6DC
New preprint on statistical inference in nonlinear #inverseproblems
Joint work with Douglas Barnes, Aditya Jambhale and Richard Nickl
@Cambridge_Uni @malga_center @UniGenova
https://t.co/1EhzhJJ397
New preprint available on adversarial examples for forward and inverse problems, with @rima_alaifari and @tandrigauksson
https://t.co/P8kUHgJ8Ks
Funded by @ERC_Research and @AFOSR
@malga_center, @UniGenova
#machinelearning, #inverseproblems, #pde
This is the usual effect when leaving for a week - but now very excited for the workshop in Oberwolfach on #machinelearning and #inverseproblems starting tomorrow!
50 graduate #students from around the world recently converged for the 2024 Gene Golub SIAM Summer School in Quito, Ecuador. The co-organizers recap the school, which focused on large-scale #InverseProblems, in SIAM News. Take a look! https://t.co/rJjUzlWQvO
Despite the #mathematical ill-posedness of #InverseProblems, several examples have recently exhibited striking performance. Martin Burger and Tim Roith highlight associated questions of reliability and trustworthiness for learning #algorithms in SIAM News: https://t.co/jbukFCBWpb
Meet the brilliant researchers working @EarthSafeEU!!
Today we are proud to introduce Prof. Andrew Valentine (@a_p_valentine) from @DurUniEarthSci
Andrew develops #computational tools to solve complex #inverseproblems relevant to critical #resources and the #EnergyTransition
Meet the brilliant researchers working @EarthSafeEU!!
Today we are proud to introduce Dr. Alba Muixí (@LaCaN_UPC), a postdoctoral researcher within the network working on #ML and ROM strategies to solve complex #inverseproblems relevant to the #EnergyTransition.
Great new work by Yihang Chen and Wenbin Li at Harbin Institute of Technology - 'Learning on the correctness class for domain #inverseproblems of #gravimetry' - https://t.co/30pTmJb18d #machinelearning #geoscience #earthscience #datascience #AI #geodynamics #gravity #geophysics
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