斯坦福大学

Characterizing the mechanical response of materials traditionally requires solving optimization problems in which model parameters are calibrated or trained to minimize the discrepancy between model predictions and experimental data. This process can be computationally expensive and time-consuming. To overcome this limitation, we propose two neural operator architectures that directly map experimentally measured data to the constitutive functions governing the mechanical response of the material: Physics-Augmented Neural Operators (PANO) and Constitutive Artificial Neural Operators (CANO). The proposed neural operators approximate the mapping between the infinite-dimensional input space of full-field displacement measurements and net reaction forces, and the infinite-dimensional output space of hyperelastic strain–energy density functions. The displacement fields are encoded through Laplacian eigenfunctions to obtain discretization-independent and noise-robust predictions. We constrain the output space to physically admissible material models that satisfy fundamental physical requirements by design. Here, we focus on isotropic, incompressible hyperelasticity and assume that the material properties are uniform across the specimen. The neural operators are trained on simulated data tuples of displacement fields and reaction force functions for a range of material models. Once trained, the neural operators enable near-instantaneous material characterization and require only a single forward pass to infer the strain–energy density function from a given experimental dataset. We test the predictive power of the neural operators for unseen data, noisy data, data with missing information, data from different spatial discretizations, and data from geometries of different sizes. We finally discuss the ill-posedness of the inverse material characterization problem and show that constraining the output function space of our neural operator framework sufficiently regularizes the problem. The trained neural operators enable rapid and robust discovery of polyconvex strain–energy density functions while avoiding the need to solve computationally expensive inverse problems.

What is the best compromise in a situation where different people value different things? The most common method for answering this question is to look at all the options, add up the utility per person associated with each, and pick the option with the largest sum. This approach seems like the obvious, theory-neutral solution, though, in fact, it relies on substantive utilitarian commitments. A second, quite common approach, involves integrating egalitarian concerns, and picking an option that trades off maximizing utility with equalizing outcomes. But there is an important, though often-ignored, third approach as well: a contractualist approach, which models what rational actors would agree to. In this paper, we test the proposals of these three competing preference aggregation algorithms in social decision-making contexts that are systematically sampled across a wide range. While the dominant approach to value aggregation up to now has been utilitarian, we find that participants strongly prefer the aggregations recommended by the contractualist algorithm, which also dominates the egalitarian approach across a range of parameterizations. Finally, we compare the judgments of large language models (LLMs) to those of our (human) participants, finding important misalignment between model and human preferences.

Implant-associated infection remains one of the leading causes of failure in orthopaedics, imposing substantial clinical and economic burdens despite advances in surgical techniques and antibiotic therapy. Conventional infection management strategies, including systemic antibiotics, local drug delivery, surface coatings, and physical surface modifications, primarily provide short-term protection. These approaches fail to deliver sustained antibacterial performance over the long service life expected for load-bearing implants. This review critically evaluates existing antibacterial strategies, highlighting their mechanisms, advantages, and translational limitations. Particular emphasis is placed on the fundamental shortcomings of surface-based approaches and external stimulus-responsive systems in long-term clinical scenarios. Building on these insights, the emerging paradigm of bulk antibacterial alloy design for infection-resistant implants is discussed. Copper, silver, and zinc are widely accepted antibacterial elements; however, they cannot be used as standalone bulk materials because they do not independently meet the mechanical, corrosion, and biocompatibility requirements of metallic implants. Among them, silver is limited by elution-driven activity and cytotoxicity concerns, zinc exhibits relatively weaker antibacterial efficacy, while copper offers a favorable balance of antibacterial performance, alloyability, and structural compatibility. Copper-, silver-, and zinc-containing alloy systems are systematically examined with respect to antibacterial efficacy, cytocompatibility, corrosion behavior, and mechanical integrity. Copper-added alloys, which offer broad-spectrum antibacterial activity through contact killing and controlled ion release while preserving structural performance when appropriately alloyed, are extensively discussed. Finally, future directions are outlined, including biodegradable antibacterial metals, multi-functional alloy design, and data-driven alloy optimisation strategies, providing a roadmap toward clinically viable, long-term infection-resistant implant materials.

Picosecond level timing resolution is a prerequisite capability for improved coincidence matching, time-of-flight measurements, and secondary vertex reconstruction. Here, we present the specification, design, and simulation results for a new Application Specific Integrated Circuit (ASIC), called PSEC6, in the TSMC 65nm process. It features 8 channels, a maximum sampling rate of 40 GSa/s, a buffer length of 204.8 nanoseconds, and a 10.24 GHz Phase Locked Loop (PLL), which is the first of its kind in the 65nm CMOS process. The event readout rate is 32 kHz, with the digitization done by an off-chip Analog-to-Digital Converter (ADC). Simulations predict a 4.0 GHz analog input bandwidth and 20 mW per channel during sampling; the 10.24 GHz PLL has a predicted jitter of 550 fs RMS at 15.7 mW. The paper describes the sampling architecture, chip signal paths, PLL design, and presents simulation results.

Flammable liquids leaking on hot surfaces are an ignition hazard for fires in aircraft, vehicles, and heavy machinery. Engineering analysis to assess fire risk in these devices must account for the flammability properties of fuels and other liquids. The autoignition temperature (AIT) is most often used for this purpose. AIT is determined following a standard procedure, such as ASTM E659, but it is well-established that the minimum ignition temperature in practical industrial configurations is often significantly higher than the AIT. In this work, we introduce a canonical experimental apparatus that addresses this issue. This proposed apparatus enables the characterization of the probabilistic hot surface ignition behavior of flammable liquids in configurations featuring thermal stratification and reduced residence times compared to ASTM E659. The apparatus is highly flexible, enabling testing with various surface materials, droplet diameters, impact velocities, and repetition rates. The introduction of automated optical diagnostics enables rapid testing of multiple fuels. The main focus of the present work is to demonstrate the repeatability of the measurements conducted in this apparatus: In repeated experiments with n-hexane, we find that the standard deviation of the hot surface ignition temperature is 3.8K. A thorough analysis of the experimental uncertainties is also conducted. We demonstrate the capabilities of the apparatus by examining fuel effects on the ignition behavior of eight fuels: n-hexane, a conventionally-derived Jet A fuel, and six synthetic aviation turbine fuels (SATFs). We find that the 50% ignition probability temperature of the most ignitable and least ignitable of our six SATFs differs by 70K, which motivates future efforts to better account for fuel effects in ignition risk analysis as new SATFs are entering the aviation fuel market.