Publications

We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as physics informed neural networks (PINNs). PINNs are popular machine-learning templates that allow for seamless integration of physical equation models with data. Their training amounts to solving an optimization problem over a weighted sum of data-fidelity and equation-fidelity objectives. Conflicts between objectives can arise from scale imbalances, heteroscedasticity in the data, stiffness of the physical equation, or from catastrophic interference during sequential training. We explain the training pathology arising from this and propose a simple yet effective inverse Dirichlet weighting strategy to alleviate the issue. We compare with Sobolev training of neural networks, providing the baseline of analytically ε-optimal training. We demonstrate the effectiveness of inverse Dirichlet weighting in various applications, including a multi-scale model of active turbulence, where we show orders of magnitude improvement in accuracy and convergence over conventional PINN training. For inverse modeling using sequential training, we find that inverse Dirichlet weighting protects a PINN against catastrophic forgetting.

Magnetic energy around astrophysical compact objects can strongly dominate over plasma rest mass. Emission observed from these systems may be fed by dissipation of Alfvén wave turbulence, which cascades to small damping scales, energizing the plasma. We use 3D kinetic simulations to investigate this process. When the cascade is excited naturally, by colliding large-scale Alfvén waves, we observe quasithermal heating with no nonthermal particle acceleration. We also find that the particles are energized along the magnetic field lines and so are poor producers of synchrotron radiation. At low plasma densities, our simulations show the transition to “charge-starved” cascades, with a distinct damping mechanism.

Magnetic energy around astrophysical compact objects can strongly dominate over plasma rest mass. Emission observed from these systems may be fed by dissipation of Alfvén wave turbulence, which cascades to small damping scales, energizing the plasma. We use 3D kinetic simulations to investigate this process. When the cascade is excited naturally, by colliding large-scale Alfvén waves, we observe quasithermal heating with no nonthermal particle acceleration. We also find that the particles are energized along the magnetic field lines and so are poor producers of synchrotron radiation. At low plasma densities, our simulations show the transition to “charge-starved” cascades, with a distinct damping mechanism.

The mammalian mitotic spindle segregates an equal number of chromosomes to daughter cells. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through a process called error correction. Despite the importance of chromosome segregation errors in many human health conditions, we lack quantitative methods to characterize the dynamic error correction process and how it is impaired in disease states. We have developed a novel experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging of spindle assembly, timed premature chromosome separation, and automated counting of kinetochores after cell division. Using our assay we targeted Aurora B kinase, a key regulator of kinetochore-microtubule attachments, with two small molecules that either inhibited Aurora B activity or perturbed its localization. While both inhibitors increased the steady state error baseline over 10-fold from control, they differed in their initial error states and times to reach steady state. Our results indicate that error correction dynamics, and not just endpoint segregation errors, are important for understanding the involvement of proteins in error correction. Future work will focus on distinguishing the functional roles of different proteins in error correction, characterizing how kinetochore-microtubule affinity and microtubule stability determine error correction dynamics, and constructing and testing a mathematical theory of error correction.

CMB-S4---the next-generation ground-based cosmic microwave background (CMB) experiment---is set to significantly advance the sensitivity of CMB measurements and enhance our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. Among the science cases pursued with CMB-S4, the quest for detecting primordial gravitational waves is a central driver of the experimental design. This work details the development of a forecasting framework that includes a power-spectrum-based semi-analytic projection tool, targeted explicitly towards optimizing constraints on the tensor-to-scalar ratio, r, in the presence of Galactic foregrounds and gravitational lensing of the CMB. This framework is unique in its direct use of information from the achieved performance of current Stage 2--3 CMB experiments to robustly forecast the science reach of upcoming CMB-polarization endeavors. The methodology allows for rapid iteration over experimental configurations and offers a flexible way to optimize the design of future experiments given a desired scientific goal. To form a closed-loop process, we couple this semi-analytic tool with map-based validation studies, which allow for the injection of additional complexity and verification of our forecasts with several independent analysis methods. We document multiple rounds of forecasts for CMB-S4 using this process and the resulting establishment of the current reference design of the primordial gravitational-wave component of the Stage-4 experiment, optimized to achieve our science goals of detecting primordial gravitational waves for r>0.003 at greater than 5σ, or, in the absence of a detection, of reaching an upper limit of r<0.001 at 95% CL.

Ensembles of particles rotating in a two-dimensional fluid can exhibit chaotic dynamics yet develop signatures of hidden order. Such "rotors" are found in the natural world spanning vastly disparate length scales - from the rotor proteins in cellular membranes to models of atmospheric dynamics. Here we show that an initially random distribution of either ideal vortices in an inviscid fluid, or driven rotors in a viscous membrane, spontaneously self assembles. Despite arising from drastically different physics, these systems share a Hamiltonian structure that sets geometrical conservation laws resulting in distinct structural states. We find that the rotationally invariant interactions isotropically suppress long wavelength fluctuations - a hallmark of a disordered hyperuniform material. With increasing area fraction, the system orders into a hexagonal lattice. In mixtures of two co-rotating populations, the stronger population will gain order from the other and both will become phase enriched. Finally, we show that classical 2D point vortex systems arise as exact limits of the experimentally accessible microscopic membrane rotors, yielding a new system through which to study topological defects.

We introduce a simple entropy-based formalism to characterize the role of mixing in pressure-balanced multiphase clouds, and demonstrate example applications using Enzo-E (magneto)hydrodynamic simulations. Under this formalism, the high-dimensional description of the system's state at a given time is simplified to the joint distribution of mass over pressure (P) and entropy (K=P/ργ). As a result, this approach provides a way for (empirically and analytically) quantifying the impact of different initial conditions and sets of physics on the system evolution. We find that mixing predominantly alters the distribution along the K direction and illustrate how the formalism can be used to model mixing and cooling for fluid elements originating in the cloud. We further confirm and generalize a previously suggested criterion for cloud growth in the presence of radiative cooling, and demonstrate that the shape of the cooling curve, particularly at the low temperature end, can play an important role in controlling condensation. Moreover, we discuss the capacity of our approach to generalize such a criterion to apply to additional sets of physics, and to build intuition for the impact of subtle higher order effects not directly addressed by the criterion.

We introduce a toy model for the time-frequency structure of fast radio bursts (FRB), in which the observed emission is produced as a narrowly-peaked intrinsic spectral energy distribution sweeps down in frequency across the instrumental bandpass as a power-law in time. Though originally motivated by emission models which invoke a relativistic shock, the model could in principle apply to a wider range of emission scenarios. We quantify the burst's detectability using the frequency bandwidth over which most of its signal-to-noise ratio (SNR) is accumulated. We demonstrate that by varying just a single parameter of the toy model-the power-law index \beta of the frequency drift rate-one can transform a long (and hence preferentially time-resolved) burst with a narrow time-integrated spectrum into a shorter burst with a broad power-law time-integrated spectrum. We suggest that burst-to-burst diversity in the value of \beta could generate the dichotomy between burst duration and frequency-width recently found by CHIME. In shock models, the value of \beta is related to the radial density profile of external medium, which in light of the preferentially longer duration of bursts from repeating sources may point to diversity in the external environments surrounding repeating versus one-off FRB sources.

A new algorithmic framework is developed for holographic coherent diffraction imaging (HCDI) based on maximum likelihood estimation (MLE). This method provides superior image reconstruction results for various practical HCDI settings, such as when data is highly corrupted by Poisson shot noise and when low-frequency data is missing due to occlusion from a beamstop apparatus. This method is also highly robust in that it can be implemented using a variety of standard numerical optimization algorithms, and requires fewer constraints on the physical HCDI setup compared to current algorithms. The mathematical framework developed using MLE is also applicable beyond HCDI to any holographic imaging setup where data is corrupted by Poisson shot noise.

The flow of water in carbon nanochannels has defied understanding thus far1, with accumulating experimental evidence for ultra-low friction, exceptionally high water flow rates and curvature-dependent hydrodynamic slippage2–5. In particular, the mechanism of water–carbon friction remains unknown6, with neither current theories7 nor classical8,9 or ab initio molecular dynamics simulations10 providing satisfactory rationalization for its singular behaviour. Here we develop a quantum theory of the solid–liquid interface, which reveals a new contribution to friction, due to the coupling of charge fluctuations in the liquid to electronic excitations in the solid. We expect that this quantum friction, which is absent in Born–Oppenheimer molecular dynamics, is the dominant friction mechanism for water on carbon-based materials. As a key result, we demonstrate a marked difference in quantum friction between the water–graphene and water–graphite interface, due to the coupling of water Debye collective modes with a thermally excited plasmon specific to graphite. This suggests an explanation for the radius-dependent slippage of water in carbon nanotubes4, in terms of the electronic excitations of the nanotubes. Our findings open the way for quantum engineering of hydrodynamic flows through the electronic properties of the confining wall.