Sebastian Goetschel
@sgoetschel.bsky.social
Computational Mathematics, TU Hamburg
Reposted by Sebastian Goetschel
Obit for Peter Lax, an applied mathematician who did pioneering work in PDEs, solitons, and computational science.
www.nytimes.com/2025/05/16/s...
www.nytimes.com/2025/05/16/s...
Peter Lax, Pre-eminent Cold War Mathematician, Dies at 99
www.nytimes.com
May 17, 2025 at 4:43 PM
Obit for Peter Lax, an applied mathematician who did pioneering work in PDEs, solitons, and computational science.
www.nytimes.com/2025/05/16/s...
www.nytimes.com/2025/05/16/s...
Reposted by Sebastian Goetschel
That problem is smooth.
And if it's not, it is differentiable everywhere.
And if it's not, we avoid the kinks almost surely.
And if we don't, what is computed is a subgradient.
And if it's not, it approximates one.
And if that's not true, who cares? The loss went down.
And if it's not, it is differentiable everywhere.
And if it's not, we avoid the kinks almost surely.
And if we don't, what is computed is a subgradient.
And if it's not, it approximates one.
And if that's not true, who cares? The loss went down.
April 27, 2025 at 10:46 AM
That problem is smooth.
And if it's not, it is differentiable everywhere.
And if it's not, we avoid the kinks almost surely.
And if we don't, what is computed is a subgradient.
And if it's not, it approximates one.
And if that's not true, who cares? The loss went down.
And if it's not, it is differentiable everywhere.
And if it's not, we avoid the kinks almost surely.
And if we don't, what is computed is a subgradient.
And if it's not, it approximates one.
And if that's not true, who cares? The loss went down.
Reposted by Sebastian Goetschel
March 4, 2025 at 11:16 AM
Reposted by Sebastian Goetschel
As someone who has reported on AI for 7 years and covered China tech as well, I think the biggest lesson to be drawn from DeepSeek is the huge cracks it illustrates with the current dominant paradigm of AI development. A long thread. 1/
January 27, 2025 at 2:12 PM
As someone who has reported on AI for 7 years and covered China tech as well, I think the biggest lesson to be drawn from DeepSeek is the huge cracks it illustrates with the current dominant paradigm of AI development. A long thread. 1/
Reposted by Sebastian Goetschel
Judith Angel, J\"orn Behrens, Sebastian G\"otschel, Marten Hollm, Daniel Ruprecht, Robert Seifried
Bathymetry reconstruction from experimental data using PDE-constrained optimisation
https://arxiv.org/abs/2404.05556
Bathymetry reconstruction from experimental data using PDE-constrained optimisation
https://arxiv.org/abs/2404.05556
April 9, 2024 at 5:14 AM
Judith Angel, J\"orn Behrens, Sebastian G\"otschel, Marten Hollm, Daniel Ruprecht, Robert Seifried
Bathymetry reconstruction from experimental data using PDE-constrained optimisation
https://arxiv.org/abs/2404.05556
Bathymetry reconstruction from experimental data using PDE-constrained optimisation
https://arxiv.org/abs/2404.05556
Reposted by Sebastian Goetschel
Reposted by Sebastian Goetschel
Thomas Baumann, Sebastian G\"otschel, Thibaut Lunet, Daniel Ruprecht, Robert Speck
Adaptive time step selection for Spectral Deferred Corrections
https://arxiv.org/abs/2403.13454
Adaptive time step selection for Spectral Deferred Corrections
https://arxiv.org/abs/2403.13454
March 21, 2024 at 4:09 AM
Thomas Baumann, Sebastian G\"otschel, Thibaut Lunet, Daniel Ruprecht, Robert Speck
Adaptive time step selection for Spectral Deferred Corrections
https://arxiv.org/abs/2403.13454
Adaptive time step selection for Spectral Deferred Corrections
https://arxiv.org/abs/2403.13454
Reposted by Sebastian Goetschel
Gayatri \v{C}aklovi\'c, Thibaut Lunet, Sebastian G\"otschel, Daniel Ruprecht
Improving Efficiency of Parallel Across the Method Spectral Deferred Corrections
https://arxiv.org/abs/2403.18641
Improving Efficiency of Parallel Across the Method Spectral Deferred Corrections
https://arxiv.org/abs/2403.18641
March 28, 2024 at 11:12 AM
Gayatri \v{C}aklovi\'c, Thibaut Lunet, Sebastian G\"otschel, Daniel Ruprecht
Improving Efficiency of Parallel Across the Method Spectral Deferred Corrections
https://arxiv.org/abs/2403.18641
Improving Efficiency of Parallel Across the Method Spectral Deferred Corrections
https://arxiv.org/abs/2403.18641