Harnessing Nonlinear Mechanics to Transform Medical Diagnostics

Discrete-time nonlinear feedback linearization via physics-informed machine learning

Comparison of the Modified Method of Averaging and the Two Variable Expansion Procedure

Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects

Translating “AI for omics” into precision therapy

Magnesium (Mg) alloys show promise for lightweight structural and biomedical applications, but they face challenges such as poor corrosion resistance and complex deformation behavior. This systematic review explores how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) address these limitations. These techniques enable the fast and accurate prediction and optimization of material properties, thereby reducing experimental effort and accelerating the design of high-performance Mg alloys. A multi-database validation approach using Scopus and Web of Science ensured methodological robustness when searching for AI, ML, and DL in Mg alloys. A comparative analysis of author keywords, index keywords, sources, authors, and countries confirmed strong thematic consistency between databases, thereby enhancing the credibility of the cluster-based bibliometric analysis. The PRISMA framework was used to ensure the structured literature search, eligibility assessment, and documentation of the selection process. 185 peer-reviewed articles (2015–2025) were analyzed and organized into seven refined thematic clusters: ‘mechanical behavior modeling using neural networks’, ‘AI-driven alloy design and compositional optimization’, ‘atomic-scale modeling and physics-guided learning’, ‘AI applications in welding and thermomechanical processing’, ‘biomaterials and microstructural optimization’, ‘corrosion modeling and degradation prediction’, ‘data-driven design and integrated optimization frameworks’. The review highlights the extensive application of models, including Artificial Neural Networks, Convolutional Neural Networks, and hybrid frameworks that combine ML with optimization algorithms or physical simulations. These approaches enhance predictions on mechanical properties, microstructural changes, corrosion behavior, and processing results of Mg alloys. The study also discusses cross-cutting themes such as simulation speed-up metrics, model interpretability across domains, and limitations in dataset coverage. Findings indicate AI-based methods can expedite alloy design and performance optimization; however, challenges remain in data accessibility, model interpretability, and experimental validation. The study concludes that integrating physics-informed ML models, using multimodal data, and employing inverse design will be crucial for advancing the intelligent development of high-performance Mg alloys for sustainable engineering applications.

The JIFT workshop on ‘‘Nonlinear dynamics and acceleration mechanisms’’ covered the following subjects:i) Stochastic properties of low dimensional nonlinear dynamical systems, ii) Nonlinear plasma phenomena and nonlinear dynamical system: a) Motion of charged particles in the tail of the geomagnetic sphere and magnetic field reconnection, b) Magnetic dynamo and chaos, c) Particle acceleration by coherent electromagnetic wave, and d) Chaos in free electron laser; iii) Chaos in chemical reactions, and iv) Stability of particle orbits in accelerators and toroidal magnetic fields in fusion devices.The plasma confinement by magnetic fields is also discussed. (AIP)

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

Deepening the scientific understanding of different phenomenology in laser powder bed fusion by an integrated framework

Perspectives on the integration between first-principles and data-driven modeling

A high-fidelity comprehensive framework for the additive manufacturing printability assessment

Bottomhole pressure (BHP) has been increasingly integrated into modern workflows in characterizing subsurface reservoir, evaluating well production performance, and optimizing artificial lift designs for unconventional reservoirs. The increasing need for agile asset development planning demands robust and continuous well performance evaluation, for which bottomhole pressure lays the foundation. However, there are several challenges that most of the unconventional operators are facing. It is uneconomical to install permanent downhole pressure gauges and have continuous measurement throughout the entire life span on all wells across the entire asset. A practical approach is to estimate BHP from wellhead pressure by using physics-based multi-phase flow correlations. However, since various multi-phase flow correlations were developed with limited field datasets and assumptions only applicable for certain flow conditions, these empirical or mechanistic models are not generalized to fully characterize the fluid flow behaviors that are applicable for various flow patterns without constant manual selection and tuning. Finally, there is a need for robust estimation of BHP with various changing wellbore configurations under different artificial lift designs and types through the life of the well. In this work, we propose a hybrid methodology integrating physics-based and machine learning models to provide BHP with high accuracy. Five different candidate multi-phase flow correlations were selected for physics-based models to estimate BHP from routine daily production data and consider any change of artificial lift designs and types. With the availability of some downhole pressure gauges to calibrate BHP estimates, we propose to improve BHP estimation in two major steps – first, selecting the best physics correlation for each producing day based on dynamic criteria using a classification method and second, improving the physics-based BHP estimate using a physics-informed machine learning (PIML) approach. The machine learning models were trained based on historical downhole gauge pressure data and validated with data hold-out in history and data measured. The results of model performance showed that this hybrid pre-trained model can be leveraged as a ‘virtual downhole gauge’ to continuously provide high-accuracy BHP estimation in a robust and consistent manner in the absence of physical downhole gauges. In this paper, we present a field case study to demonstrate the deployment and usage of continuous BHP estimation integrating physics-based and machine learning models. This framework has been successfully deployed for one of the largest U.S. unconventional shale basins with over 3000 producing wells. By leveraging this hybrid methodology, high-accuracy and continuous BHP estimates can be provided at field or asset level to streamline well performance analytics workflows for unconventional reservoirs and facilitate better asset development decision-making.

Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational costs of detailed mechanistic models while leveraging the predictive power of machine learning. This study applies this fusion to the biomedical challenge of A fibril aggregation, a key factor in Alzheimer’s disease. Central to the research is the introduction of an automatic reaction order model reduction framework, designed to optimize reduced-order kinetic models. This framework represents a shift in model construction, automatically determining the appropriate level of detail for reaction network modeling. The proposed approach significantly improves simulation efficiency and accuracy, particularly in systems like A aggregation, where precise modeling of nucleation and growth kinetics can reveal potential therapeutic targets. Additionally, the automatic model reduction technique has the potential to generalize to other network models. The methodology offers a scalable and adaptable tool for applications beyond biomedical research. Its ability to dynamically adjust model complexity based on system-specific needs ensures that models remain both computationally feasible and scientifically relevant, accommodating new data and evolving understandings of complex phenomena.

<p>Evapotranspiration (ET) is a central water flux in the global hydrological cycle, closely coupled to the energy balance and carbon cycle. It is primarily governed by non-linear energy processes controlled by meteorological conditions as well as different heterogeneous properties of the ecosystem. Various physical models of ET are widely used, such as the Penman-Monteith (PM) equation, which preserves physical laws and accounts for phenomenological behavior. However, these mechanistic models are often subject to large uncertainties, largely due to the limited understanding of the biological controls, particularly how plants control the land-to-atmosphere water flux by closing and opening their stomata.</p><p>Here, we propose a data-adaptive hybrid modeling approach for ET that combines the physics-based PM equation with machine learning (ML) by inferring the biological and aerodynamic regulator of the evaporative water flux from observations. Specifically, the framework comprises setting up a feed-forward neural network and integrating the physically-constraining PM equation in the loss function of the latent heat flux (LE). The stomatal resistance (r<sub>s</sub>) and aerodynamic resistance (r<sub>a</sub>) are modeled as intermediate latent variables, based on micro-meteorological observations collected and curated in the FLUXNET database.<strong> </strong>For baseline comparison, two conceptually different ML models have been set up, where the first model simulates LE directly without imposing any physical constraints and the second model is an alternative pseudo-hybrid model approach [1], where the main distinction lies in the formulation of the loss function.</p><p> Our hybrid model is capable of capturing the diurnal and seasonal variations between the mean values of predicted and observed LE. The obtained data-driven parameterizations of the latent variables r<sub>s </sub>and r<sub>a</sub> are evaluated against the micro-meteorological conditions to validate their physical plausibility.  We show that our hybrid modeling approach not only improves the rigid ad-hoc formulations of mechanistic models using observations, but also that hybrid models provide interpretable results that obey the physical laws of energy and mass conservation, in contrast to black-box ML models.</p><p>Our presented hybrid modeling approach can be extended to global generalizations of LE flux estimates and serve as observation-based parameterizations of r<sub>s </sub>and r<sub>a</sub> in complex land surface and Earth system models.</p><p>[1]      W. L. Zhao <em>et al.</em>, “Physics‐Constrained Machine Learning of Evapotranspiration,” <em>Geophys. Res. Lett.</em>, vol. 46, no. 24, pp. 14496–14507, Dec. 2019, doi: 10.1029/2019GL085291.</p>

Diverse fields of modern science, including mathematical modeling and physics, plasma physics, atmospheric sciences, marine sciences, hydrodynamics, nonlinear mechanics, and other complex nonlinear physical phenomena, are expressed through Nonlinear Partial Differential Equations (NLPDEs). Exact solutions play an important role in understanding the behavior of solitary wave solutions and the dynamical properties of significant outcomes for higher-dimensional NLPDEs. As an integrable extension of the nonlinear Schrödinger equation, the nonlocal Kundu-Eckhaus model comprehends higher-order nonlinearities and nonlocal effects. The stability properties of dark, bright, periodic multiple, and singular soliton solutions to the nonlocal Kundu-Eckhaus (KE) equation within the Parity-Time (PT) symmetry model are studied here, whereas past studies have neglected the effects of nonlocal interactions and PT symmetry. is a well-established method, particularly for depicting wave dynamics in nonlinear optical and quantum systems. We attain exact traveling wave solutions evolved in hyperbolic and trigonometric forms, representing several classes of solitons by applying this method. The findings elucidate that the obtained soliton solutions are stable under small perturbations, highlighting their robustness and the persistence of localized waveforms in nonlocal circumstances. We plotted 3D, 2D, and contour graphs for simulating our findings using MATLAB. This research sheds light on the mysterious understanding of nonlocal nonlinear wave behaviors and offers analytical methodologies for future scrutiny in fields such as optical physics and complex nonlinear systems.

Bose-Einstein condensates of exciton-polaritons in inorganic semiconductor microcavities are known to possess strong interparticle interactions attributed to their excitonic component. The interactions play a crucial role in the nonlinear dynamics of such systems and can be witnessed as the high energy shift of polariton states. However, the localised nature of Frenkel excitons in strongly coupled organic microcavities precludes interparticle Coulomb exchange-interactions that affect nonlinear dynamics and change mechanisms of polariton energy shifts accordingly. We have scrutinized the origins of energy shifts in connection with nonlinear dynamics in Frenkel exciton-polariton condensates and examined the possible contributions: intracavity optical Kerr-effect, gain-induced frequency pulling, polariton interactions and effects related to saturation of optical transitions for "dark"- and "bright" molecules [1]. Unlike the conventional strongly coupled semiconductor microcavities, we have shown that nonlinear interactions within the condensate do not rely on polariton interactions but instead originated from Pauli-blocking principle forbidding double excitation of the same molecular states. We have developed a theoretical model explaining the omnipresent energy shift of the condensate wavefunction together with its spectral and polarization features at the BEC transition in a consistent way. The crucial role of intermolecular energy transfer and “dark” exciton reservoir has been demonstrated for the first time. We believe the principles explored in this work are relevant to other systems exhibiting strong coupling of Frenkel excitons with a cavity mode, regardless of a cavity type, whether one dealing with Fabry-Perot cavities or plasmonic nanocavities etc and; therefore, provide general insight on nonlinear phenomena in composite light-matter condensates. [1] T. Yagafarov, D. Sannikov, A. Zasedatelev, K. Georgiou, A. Baranikov, O. Kyriienko, I. Shelykh, L. Gai, Z. Shen, D. G. Lidzey, P. Lagoudakis, Mechanisms of blueshifts in organic polariton condensates, Commun Phys 3, 18 (2020). https://doi.org/10.1038/s42005-019-0278-6