We model and experimentally validate a nonlinear energy harvester capable of bidirectional hysteresis. In particular, both hardening softening response within the quadratic potential field power generating piezoelectric beam (with permanent magnet end mass) is invoked by tuning magnetic interactions. Not only this technique shown to increase bandwidth device but experimental results additionally verify capability outperform linear resonance. Engaging phenomenon ideally suited efficiently...

We propose and experimentally validate a first-principles based model for the nonlinear piezoelectric response of an electroelastic energy harvester. The analysis herein highlights importance modeling inherent nonlinearities that are not limited to higher order elastic effects but also include coupling power harvesting circuit. Furthermore, damping mechanism is shown accurately restrict amplitude bandwidth frequency response. linear framework widely accepted theoretical investigations...

Nonlinear piezoelectric effects in flexural energy harvesters have recently been demonstrated for drive amplitudes well within the scope of anticipated vibration environments power generation. In addition to strong softening effects, steady-state oscillations are highly damped as well. fluid damping was previously employed successfully model dependent decreases frequency response due high-velocity oscillations, but this article instead harmonizes with a body literature concerning weakly...

Knowledge distillation is a popular technique for training small student network to emulate larger teacher model, such as an ensemble of networks. We show that while knowledge can improve generalization, it does not typically work commonly understood: there often remains surprisingly large discrepancy between the predictive distributions and student, even in cases when has capacity perfectly match teacher. identify difficulties optimization key reason why unable also how details dataset used...

The translation equivariance of convolutional layers enables neural networks to generalize well on image problems. While provides a powerful inductive bias for images, we often additionally desire other transformations, such as rotations, especially non-image data. We propose general method construct layer that is equivariant transformations from any specified Lie group with surjective exponential map. Incorporating new requires implementing only the and logarithm maps, enabling rapid...

This letter investigates the nonlinear response of a bimorph energy harvester comprised lead zirconate titanate (PZT-5A) laminates. For near resonant excitations, we demonstrate significant intrinsic behavior despite geometrically linear motion. Fourth order elastic and electroelastic tensor values for PZT-5A are identified following methods recently published concerning PZT-5H bimorph. A trend indicative dissipative mechanism is discussed as well inadequacy modeling. The exhibits an...

A review of past and recent developments in multiaxial excitation linear nonlinear structures is presented. The objective to some the basic approaches used analytical experimental methods for kinematic dynamic analysis flexible mechanical systems, identify future directions this research area. In addition, comparison between uniaxial excitations their impact on a structure’s life-cycles provided. importance understanding failure mechanisms complex has led development vast range theoretical,...

We present an alternative approach to the analysis of nonlinear systems with long-term memory that is based on Koopman operator and a Lévy transformation in time. Memory effects are considered be result interactions between system its surrounding environment. The leads decomposition into modes whose temporal behavior anomalous lacks characteristic scale. On average, time evolution mode follows Mittag-Leffler function, can described using fractional calculus. general theory demonstrated...

A popular approach to protein design is combine a generative model with discriminative for conditional sampling. The samples plausible sequences while the guides search high fitness. Given its broad success in sampling, classifier-guided diffusion modeling promising foundation design, leading many develop guided models structure inverse folding recover sequences. In this work, we propose diffusioN Optimized Sampling (NOS), guidance method discrete that follows gradients hidden states of...

Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases. These models, however, are challenging to apply many real world systems, those that don't conserve energy contain contacts, a common setting for robotics and reinforcement learning. In this paper, we examine the biases make physics-inspired successful in practice. We show that, contrary conventional wisdom, improved...

Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization. However, its adoption drug design has been hindered by the discrete, high-dimensional nature of decision variables. We develop new approach (LaMBO) which jointly trains denoising autoencoder with discriminative multi-task Gaussian process head, allowing gradient-based multi-objective acquisition functions in latent space autoencoder. These allow LaMBO to balance explore-exploit tradeoff over...

Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in vast design space of biological sequences. Whereas it is possible to optimize various properties interest jointly using multi-objective acquisition function, such as expected hypervolume improvement (EHVI), this approach does not account objectives with hierarchical dependency structure. We consider common use case where some regions Pareto frontier are prioritized over others...

Piezoelectric materials constitute an efficient transduction medium for passive power generation from ambient vibrations. As such, the unimorph and bimorph piezoelectric laminate linear beam is a prolifically researched energy harvesting device. The modeling framework amenable to analytical solutions frequency matching inertial generators environmental oscillations seemingly ideal solution. Realistically, however, disturbances are rarely of one particular oscillators capable strong responses...

With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are natural choice for online decision making. However, typically require at least $\mathcal{O}(n^2)$ computations $n$ training points, limiting their general applicability. Stochastic variational (SVGPs) can provide scalable inference dataset fixed size, but difficult to efficiently condition on new data. We propose conditioning (OVC), procedure SVGPs in an setting that does not...

This paper investigates an approach to passively eliminate subharmonic responses in nonlinear oscillators forced at resonance. A general framework is developed for identifying asymptotic levels where parameter values can be tuned achieve dynamic cancellation of subharmonics. The results apply with arbitrary structure. Due its breadth application from atomic force microscopy aeromechanics, the methodology demonstrated using a reduced-order model inextensible cantilevered beam carrying...