Andrei A. Klishin<p>re-<a href="https://fediscience.org/tags/introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>introduction</span></a><br>Hi Fediscience! I am an Assistant Professor of Mechanical Engineering at University of Hawaiʻi at Mānoa (Honolulu). I got here starting from Physics training with many scientific detours into data-driven models, complex systems, nanomaterial self-assembly, human learning of complex networks, naval ships, and design problems.<br>I grew up in Belarus and have *opinions* on that region of the world. I've been on Fediverse since late 2022 when *something* happened to our previous cybersocial infrastructure, but the previous server I was on is sunsetting. Please come say hi and recommend cool people to follow here.<br>I have a blog with longer thoughts on science-adjacent topics.<br><a href="https://www.aklishin.science/blog/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">aklishin.science/blog/</span><span class="invisible"></span></a><br><a href="https://fediscience.org/tags/ComplexSystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComplexSystems</span></a> <a href="https://fediscience.org/tags/NetworkScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NetworkScience</span></a> <a href="https://fediscience.org/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> <a href="https://fediscience.org/tags/DynamicalSystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DynamicalSystems</span></a> <a href="https://fediscience.org/tags/CollectiveBehavior" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CollectiveBehavior</span></a> <a href="https://fediscience.org/tags/StatisticalPhysics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>StatisticalPhysics</span></a></p>
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#dynamicalsystems
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Khurram Wadee ✅<p>A few days back, I posted some <a href="https://mastodon.org.uk/tags/AnimatedGifs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AnimatedGifs</span></a> of the exact solution for a large-amplitude undamped, unforced <a href="https://mastodon.org.uk/tags/Pendulum" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pendulum</span></a>. I then thought to complete the study to include the case when it has been fed enough <a href="https://mastodon.org.uk/tags/energy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>energy</span></a> to allow it just to undergo <a href="https://mastodon.org.uk/tags/FullRotations" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FullRotations</span></a>, rather than just <a href="https://mastodon.org.uk/tags/oscillations" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>oscillations</span></a>. Well, it turns out that it is “a bit more complicated than I first expected” but I finally managed it.</p><p><a href="https://mastodon.org.uk/tags/Mathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Mathematics</span></a> <a href="https://mastodon.org.uk/tags/AppliedMathematics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AppliedMathematics</span></a> <a href="https://mastodon.org.uk/tags/SpecialFunctions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpecialFunctions</span></a> <a href="https://mastodon.org.uk/tags/DynamicalSystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DynamicalSystems</span></a> <a href="https://mastodon.org.uk/tags/NonlinearPhenomena" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NonlinearPhenomena</span></a></p>
DurstewitzLab<p>Can time series (TS) <a href="https://mathstodon.xyz/tags/FoundationModels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FoundationModels</span></a> (FM) like Chronos zero-shot generalize to unseen <a href="https://mathstodon.xyz/tags/DynamicalSystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DynamicalSystems</span></a> (DS)? <a href="https://mathstodon.xyz/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a></p><p>No, they cannot!</p><p>But *DynaMix* can, the first TS/DS foundation model based on principles of DS reconstruction, capturing the long-term evolution of out-of-domain DS: <a href="https://arxiv.org/pdf/2505.13192v1" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/pdf/2505.13192v1</span><span class="invisible"></span></a></p><p>Unlike TS foundation models, DynaMix exhibits <a href="https://mathstodon.xyz/tags/ZeroShotLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ZeroShotLearning</span></a> of long-term stats of unseen DS, incl. attractor geometry & power spectrum, w/o *any* re-training, just from a context signal. <br>It does so with only 0.1% of the parameters of Chronos & 10x faster inference times than the closest competitor.</p><p>It often even outperforms TS FMs on forecasting diverse empirical time series, like weather, traffic, or medical data, typically used to train TS FMs. <br>This is surprising, cos DynaMix’ training corpus consists *solely* of simulated limit cycles & chaotic systems, no empirical data at all!</p><p>And no, it’s neither based on Transformers nor Mamba – it’s a new type of mixture-of-experts architecture based on the recently introduced AL-RNN (<a href="https://proceedings.neurips.cc/paper_files/paper/2024/file/40cf27290cc2bd98a428b567ba25075c-Paper-Conference.pdf" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">proceedings.neurips.cc/paper_f</span><span class="invisible">iles/paper/2024/file/40cf27290cc2bd98a428b567ba25075c-Paper-Conference.pdf</span></a>), specifically trained for DS reconstruction.</p><p>Remarkably, DynaMix not only generalizes zero-shot to novel DS, but it can even generalize to new initial conditions and regions of state space not covered by the in-context information.</p><p>We dive a bit into the reasons why current time series FMs not trained for DS reconstruction fail, and conclude that a DS perspective on time series forecasting & models may help to advance the <a href="https://mathstodon.xyz/tags/TimeSeriesAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TimeSeriesAnalysis</span></a> field.</p>
Alexander Hölken<p>My commentary on our 2023 LIDA paper just got published! In it, I explore the idea that the behavioral and cognitive dispositions our original paper was concerned with may be understood as topological features of cognitive subsystems: </p><p><a href="https://journals.sagepub.com/doi/10.1177/00368504241245812" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">journals.sagepub.com/doi/10.11</span><span class="invisible">77/00368504241245812</span></a></p><p><a href="https://discuss.systems/tags/CogSci" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CogSci</span></a> <a href="https://discuss.systems/tags/Dispositions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Dispositions</span></a> <a href="https://discuss.systems/tags/DynamicalSystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DynamicalSystems</span></a> <a href="https://discuss.systems/tags/LIDA" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LIDA</span></a></p>
Paul Marrow 🇪🇺<p><a href="https://ecoevo.social/tags/ecoevo" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ecoevo</span></a> folks <a href="https://ecoevo.social/tags/introductions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>introductions</span></a> <a href="https://ecoevo.social/tags/introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>introduction</span></a>. I am a former <a href="https://ecoevo.social/tags/evolutionary" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>evolutionary</span></a> <a href="https://ecoevo.social/tags/ecologist" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ecologist</span></a>. DPhil <a href="https://ecoevo.social/tags/UYorkUK" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UYorkUK</span></a> <a href="https://ecoevo.social/tags/evolutionarydynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>evolutionarydynamics</span></a> (<a href="https://ecoevo.social/tags/adaptivedynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>adaptivedynamics</span></a>) <a href="https://ecoevo.social/tags/populationdynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>populationdynamics</span></a> <a href="https://ecoevo.social/tags/gametheory" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gametheory</span></a> <a href="https://ecoevo.social/tags/dynamicalsystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dynamicalsystems</span></a> <a href="https://ecoevo.social/tags/evolutionarystablestrategies" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>evolutionarystablestrategies</span></a> <a href="https://ecoevo.social/tags/ESS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ESS</span></a>. Postdoc <a href="https://ecoevo.social/tags/ULeiden" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ULeiden</span></a> continued this. Postdoc <a href="https://ecoevo.social/tags/UCambridge" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UCambridge</span></a> <a href="https://ecoevo.social/tags/lifehistoryevolution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>lifehistoryevolution</span></a> <a href="https://ecoevo.social/tags/soaysheepproject" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>soaysheepproject</span></a> <a href="https://ecoevo.social/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://ecoevo.social/tags/reproductivestrategies" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>reproductivestrategies</span></a> <a href="https://ecoevo.social/tags/dynamicprogramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dynamicprogramming</span></a> <a href="https://ecoevo.social/tags/fielddata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>fielddata</span></a> <br>Where did this lead to? Recruitment into business for <a href="https://ecoevo.social/tags/bioinspiredcomputing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bioinspiredcomputing</span></a></p>
Jitse Niesen<p>Hello everybody, here is my <a href="https://mathstodon.xyz/tags/introduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>introduction</span></a>.</p><p>I am Jitse (he/him), originally from the Netherlands but working in the School of Mathematics at the University of <a href="https://mathstodon.xyz/tags/Leeds" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Leeds</span></a>, UK for 10+ years. Research interests: <a href="https://mathstodon.xyz/tags/NumericalAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NumericalAnalysis</span></a> and <a href="https://mathstodon.xyz/tags/DynamicalSystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DynamicalSystems</span></a>. Lately working on Fourier extension, geometric numerical integration and applications in particle methods in plasma physics and compartments models in chemistry. I contribute to <a href="https://mathstodon.xyz/tags/Spyder" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Spyder</span></a>, an open=source IDE for Python geared towards scientists.</p>
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