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DTSTART;TZID=America/Detroit:20231019T110000
DTEND;TZID=America/Detroit:20231019T130000
DTSTAMP:20231025T194805Z
CREATED:20230915T150343Z
LAST-MODIFIED:20231025T194805Z
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SUMMARY:SciML Webinar Ji Qi: DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling for Robust Training of Machine Learning Interatomic Potentials
DESCRIPTION:https://umich.zoom.us/j/95111677727?pwd=V1Q5MkUwT2NpOFVhd0ZRVGR1YTM3Zz09 \n\nSpeaker: Ji Qi (UC San Diego and LLNL)\nSession Chair: Daniel Schwalbe-Koda (UC Los Angeles) \nAbstract: Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond conventional first-principles approaches\, and they have played increasingly important roles in understanding and design of materials. However\, MLIPs are only as accurate and robust as the data they are trained on. In this seminar\, I will present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements\, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolate more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with universal potentials such as M3GNet can be used in place of expensive ab initio MD to rapidly create a large configuration space for target materials systems. For demonstration\, we combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. \nIn this seminar\, I will walk through two Jupiter notebooks to showcase DIRECT sampling with the two example cases demonstrated in our manuscript\, so that audience can expect to reproduce our major results with no trouble. Hopefully\, DIRECT sampling will serve as a straightforward\, efficient\, useful plug-in for the robust training of MLIPs across any compositional complexity.
URL:https://micde.umich.edu/event/workshop-seminarsciml-webinar-ji-qi-2/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231012T110000
DTEND;TZID=America/Detroit:20231012T130000
DTSTAMP:20231018T173554Z
CREATED:20230915T150330Z
LAST-MODIFIED:20231018T173554Z
UID:10000644-1697108400-1697115600@micde.umich.edu
SUMMARY:SciML Webinar Justin Beroz: A closed-form mathematical framework for modeling turbulent fluids
DESCRIPTION:Speaker: Justin Beroz (ReynKo Inc.) \n\nSession Chair: Varun Shankar (Physics Inverted Mataerials) \nAbstract: Despite significant advances over the past two centuries\, a complete general mathematical framework for turbulent fluid motion has yet to be put forth\, and remains the longest standing unsolved problem in classical physics. I will present such a framework\, which is based on constructing a spectral decomposition for the fluid’s kinetic energy from first principles. The approach departs from the usual Reynolds decomposition and yields a set of closed and solvable ordinary differential equations in matrix form. Within this prescription\, the linear terms in the Navier-Stokes equations correspond to a symmetric matrix operator\, and the nonlinear convective term enters as an anti-symmetric operator that provides coupling between eigenstates of turbulent fluctuation. Specifically\, I will present a derivation for the turbulent energy spectrum\, including the Kolmogorov energy cascade; elucidate instability mechanisms for the transition to turbulence;  and detail the analytical solution for turbulence in a box. Careful attention will be given to the physical picture and scaling\, in addition to the rigorous mathematical program. The talk will conclude with a forward look into current efforts implementing the model into a numerical simulation within my company\, ReynKo Inc.
URL:https://micde.umich.edu/event/workshop-seminarsciml-webinar-justin-beroz/
CATEGORIES:Micde,Scientific Computing,Sciml,SciML Webinar Series,Webinar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20231005T110000
DTEND;TZID=America/Detroit:20231005T123000
DTSTAMP:20231018T165056Z
CREATED:20230918T024126Z
LAST-MODIFIED:20231018T165056Z
UID:10000649-1696503600-1696509000@micde.umich.edu
SUMMARY:SciML Webinar: Jianke Yang - Generative Adversarial Symmetry Discovery
DESCRIPTION:Speaker: Jianke Yang (UC San Diego) \n\n\nSession Chair: Bharath Ramsundar (Deep Forest Sciences) \nAbstract:Despite the success of equivariant neural networks in scientific applications\, they require knowing the symmetry group a priori. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivariance from data.  We propose a framework\, LieGAN\, to automatically discover equivariances from a dataset using a paradigm akin to generative adversarial training. Specifically\, a generator learns a group of transformations applied to the data\, which preserves the original distribution and fools the discriminator. LieGAN represents symmetry as an interpretable Lie algebra basis and can discover various symmetries such as the rotation group and the restricted Lorentz group in trajectory prediction and top-quark tagging tasks. More generally\, LieGAN can also be extended to discover the nonlinear symmetries in high-dimensional dynamics. The learned symmetry can be readily used in several existing equivariant neural networks to improve prediction accuracy and generalization. It can also improve the symbolic equation discovery and long-term forecasting for various dynamical systems. \n\n\n 
URL:https://micde.umich.edu/event/sciml-webinar-jianke-yang-generative-adversarial-symmetry-discovery/
CATEGORIES:Sciml,SciML Webinar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230928T110000
DTEND;TZID=America/Detroit:20230928T123000
DTSTAMP:20231018T163755Z
CREATED:20230918T023335Z
LAST-MODIFIED:20231018T163755Z
UID:10000648-1695898800-1695904200@micde.umich.edu
SUMMARY:SciML Webinar: Bob Carpenter - Multiscale Generalized Hamiltonian Monte Carlo with Delayed Rejection
DESCRIPTION:Speaker: Bob Carpenter (Flatiron Institute) \n\n\nSession Chair: Sam Livingstone (University College London) \n\n\nAbstract: In this talk\, I will demonstrate how we can combine two ideas\, generalized Hamiltonian Monte Carlo and delayed rejection\, to derive a sampler that is as efficient as Hamiltonian Monte Carlo\, but is able to adapt its step size to deal with multiscale distributions\, much like a standard integrator for ordinary differential equations. A distribution is multiscale if its curvature has different scales in the posterior; a textbook example is Radford Neal’s funnel example derived from hierarchical priors\, which has a very flat mouth (corresponding to high population variance) and very highly curved neck (low population variance). No fixed step size allows exploration of its posterior. Generalized HMC allows us to take a single Hamiltonian step along the gradient at a time (which is equivalent to Metropolis-adjusted Langevin dynamics)\, but only refresh momentum partially (which makes it underdamped). The naive form of this algorithm does not work because momentum must be reversed to maintain detailed balance if the Metropolis step rejects. To maintain directed exploration\, we apply delayed rejection\, which allows a proposal rejected due to divergence of the Hamiltonian (from too large a step size in the first-order approximation of the dynamics) to be retried with a smaller step size (with a Hastings-style adjustment for the retry). We show that the combination of delayed rejection and GHMC allows sampling multiscale distributions which otherwise lead to biased samples in standard Hamiltonian Monte Carlo (including dynamic forms such as the no-U-turn sampler). In conclusion\, I will discuss some preliminary work on applying the the automatic tuning method using complementary parallel chains developed by Matt Hoffman and Pavel Sountsov for their sampler MEADS (which also uses generalized HMC\, but with an alternative approach to maintaining directed exploration based on work of Radford neal\, which will also describe). \n\nSlides: https://statmodeling.stat.columbia.edu/wp-content/uploads/2023/09/carpenter-sciml-webinar-2023.pdf
URL:https://micde.umich.edu/event/sciml-webinar-bob-carpenter-multiscale-generalized-hamiltonian-monte-carlo-with-delayed-rejection/
CATEGORIES:Sciml,SciML Webinar Series
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