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From the observed nonlinear dynamics we compute numerical derivatives and nonlinear library functions. Each set of library functions is associated with a free energy that determines the fit landscape. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/9c6e3e1e-2cc5-4674-a7cf-18146509fa5e/sensors_SST.jpg Research Data-driven sensors selected for the Sea Surface Temperature dataset (white markers) plotted over the temperature field reconstruction uncertainty [1]. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/651006d9-c1be-4e46-93f1-cb8e247fb975/qian_misrep.png Research When presented with the network A, a human is likely to remember a shuffled version of it f(A). By emphasizing the within-module edges in network A*, we make the learned network f(A*) closer to the intended network A [1]. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/0c947b23-e592-4708-a2c9-6aceaccac232/teich_citation.png Research Across a range of physics subfields, articles where both first and last author are men (MM) are consistently overcited with respect to expected citation rate, while articles with at least the first or last woman author (W||W) are consistently undercited. The inequity varies by the specific subfield, being the most dire in general physics, and comparatively smaller in astronomy [1]. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/1616722794706-NROV62WRKP2BTK2VAZ9U/TripleA-1.png Research Complex systems design raises questions about the paterns of arrangement across the whole ensemble of possible design solutions. Systems Physics allows, among other things, mapping out these patterns, such as avoidance, adjacency, and association [3]. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/1616722551775-CMHN7C2TU3J4ME27QR3U/fig1s_design_space-1.png Research (a) Self-assembly can produce structures of varying sequence, length, and topology. (b) The self-assembly design space is spanned by the binding energy, chemical potential, and bond rigidity. (c) All structures can be enumerated with techniques based on transfer matrices. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/1616722411929-99FTIBO65T42B7VW5SE5/entropy_figure-1.png Research All entropically ordering systems can be arranged on the axis of “sea entropy“, or the number of states that system can take if two particle positions are fixed. Sea entropy decreases from 3D particles with depletants (a), to 3D particles without depletants (b), to 2D particles in continuous space (c), to, finally, 2D particles on a lattice (d). Measuring entropic ordering in (d) thus provides a lowest bound and sufficient condition for the whole axis. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/1616722925512-I6FOBRINZWHQQA4MWBUX/tensor_network_gb-1.png Research Tensor networks form an information structure about the design space. While the initial topology (a) computes the partition function Z, we can add various modifications (bcd), and recombine them into specific queries (efg) yielding knowledge about the design space. https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/1616723024838-57LI3MITNJEFG1VOJ8DZ/ApJ_2016.png Research Accretion of new parcels of mass (square) to existing dense cores (circles) is driven by both the distance between them and their mass, akin to preferential attachment in network models. This interplay, combined with the fractal distribution in space, yields the power-law distribution of star masses. https://www.aklishin.science/outreach daily 0.75 2022-02-18 https://www.aklishin.science/join daily 0.75 2025-03-05 https://images.squarespace-cdn.com/content/v1/60579eb4f24ba86fbbe613c1/0fd61272-89da-4181-b4be-56a290bb94b6/CEEHIad2.jpg https://www.aklishin.science/contact daily 0.75 2025-05-06