Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin
et al.
We propose a physics-aware Consistency Training (CT) method that accelerates sampling in Diffusion Models with physical constraints. Our approach leverages a two-stage strategy: (1) learning the noise-to-data mapping via CT, and (2) incorporating physics constraints as a regularizer. Experiments on toy examples show that our method generates samples in a single step while adhering to the imposed constraints. This approach has the potential to efficiently solve partial differential equations (PDEs) using deep generative modeling.
While current AI-driven methods excel at deriving empirical models from individual experiments, a significant challenge remains in uncovering the common fundamental physics that underlie these models -- a task at which human physicists are adept. To bridge this gap, we introduce AI-Newton, a novel framework for concept-driven scientific discovery. Our system autonomously derives general physical laws directly from raw, multi-experiment data, operating without supervision or prior physical knowledge. Its core innovations are twofold: (1) proposing interpretable physical concepts to construct laws, and (2) progressively generalizing these laws to broader domains. Applied to a large, noisy dataset of mechanics experiments, AI-Newton successfully rediscovers foundational and universal laws, such as Newton's second law, the conservation of energy, and the universal gravitation. This work represents a significant advance toward autonomous, human-like scientific discovery.
Lan Thi Ha Nguyen, Kien Ton Manh, Anh Do Duc
et al.
Self-supervised goal-conditioned reinforcement learning enables robots to autonomously acquire diverse skills without human supervision. However, a central challenge is the goal setting problem: robots must propose feasible and diverse goals that are achievable in their current environment. Existing methods like RIG (Visual Reinforcement Learning with Imagined Goals) use variational autoencoder (VAE) to generate goals in a learned latent space but have the limitation of producing physically implausible goals that hinder learning efficiency. We propose Physics-Informed RIG (PI-RIG), which integrates physical constraints directly into the VAE training process through a novel Enhanced Physics-Informed Variational Autoencoder (Enhanced p3-VAE), enabling the generation of physically consistent and achievable goals. Our key innovation is the explicit separation of the latent space into physics variables governing object dynamics and environmental factors capturing visual appearance, while enforcing physical consistency through differential equation constraints and conservation laws. This enables the generation of physically consistent and achievable goals that respect fundamental physical principles such as object permanence, collision constraints, and dynamic feasibility. Through extensive experiments, we demonstrate that this physics-informed goal generation significantly improves the quality of proposed goals, leading to more effective exploration and better skill acquisition in visual robotic manipulation tasks including reaching, pushing, and pick-and-place scenarios.
Artificial neural networks (ANNs), which are inspired by the brain, are a central pillar in the ongoing breakthrough in artificial intelligence. In recent years, researchers have examined mechanical implementations of ANNs, denoted as Physical Neural Networks (PNNs). PNNs offer the opportunity to view common materials and physical phenomena as networks, and to associate computational power with them. In this work, we incorporated mechanical bistability into PNNs, enabling memory and a direct link between computation and physical action. To achieve this, we consider an interconnected network of bistable liquid-filled chambers. We first map all possible equilibrium configurations or steady states, and then examine their stability. Building on these maps, both global and local algorithms for training multistable PNNs are implemented. These algorithms enable us to systematically examine the network's capability to achieve stable output states and thus the network's ability to perform computational tasks. By incorporating PNNs and multistability, we can design structures that mechanically perform tasks typically associated with electronic neural networks, while directly obtaining physical actuation. The insights gained from our study pave the way for the implementation of intelligent structures in smart tech, metamaterials, medical devices, soft robotics, and other fields.
I dissent from the standard assertion of a "Two Times Problem," in which physical time is taken as being at odds with the human sense of a "flow of time." I provide a brief overview of the case to be made for the contrary view: namely, that physical theory is indeed consistent with a genuine temporal dynamism that takes into account the quantum level in connection with spacetime emergence, the latter being supervenient on specific quantum processes.
Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Symbolic Regression is the study of algorithms that automate the search for analytic expressions that fit data. While recent advances in deep learning have generated renewed interest in such approaches, the development of symbolic regression methods has not been focused on physics, where we have important additional constraints due to the units associated with our data. Here we present $Φ$-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints. Our system is built, from the ground up, to propose solutions where the physical units are consistent by construction. This is useful not only in eliminating physically impossible solutions, but because the "grammatical" rules of dimensional analysis restrict enormously the freedom of the equation generator, thus vastly improving performance. The algorithm can be used to fit noiseless data, which can be useful for instance when attempting to derive an analytical property of a physical model, and it can also be used to obtain analytical approximations to noisy data. We test our machinery on a standard benchmark of equations from the Feynman Lectures on Physics and other physics textbooks, achieving state-of-the-art performance in the presence of noise (exceeding 0.1%) and show that it is robust even in the presence of substantial (10%) noise. We showcase its abilities on a panel of examples from astrophysics.
Matthew Chan, Nathaniel Snyder, Marcus Lucas
et al.
Although organizations are continuously making concerted efforts to harden their systems against network attacks by air-gapping critical systems, attackers continuously adapt and uncover covert channels to exfiltrate data from air-gapped systems. For instance, attackers have demonstrated the feasibility of exfiltrating data from a computer sitting in a Faraday cage by exfiltrating data using magnetic fields. Although a large body of work has recently emerged highlighting various physical covert channels, these attacks have mostly targeted open-loop cyber-physical systems where the covert channels exist on physical channels that are not being monitored by the victim. Network architectures such as fog computing push sensitive data to cyber-physical edge devices--whose physical side channels are typically monitored via state estimation. In this paper, we formalize covert data exfiltration that uses existing cyber-physical models and infrastructure of individual devices to exfiltrate data in a stealthy manner, i.e., we propose a method to circumvent cyber-physical state estimation intrusion detection techniques while exfiltrating sensitive data from the network. We propose a generalized model for encoding and decoding sensitive data within cyber-physical control loops. We evaluate our approach on a distributed IoT network that includes computation nodes residing on physical drones as well as on an industrial control system for the control of a robotic arm. Unlike prior works, we formalize the constraints of covert cyber-physical channel exfiltration in the presence of a defender performing state estimation.
The design of attacks for cyber physical systems is critical to assess CPS resilience at design time and run-time, and to generate rich datasets from testbeds for research. Attacks against cyber physical systems distinguish themselves from IT attacks in that the main objective is to harm the physical system. Therefore, both cyber and physical system knowledge are needed to design such attacks. The current practice to generate attacks either focuses on the cyber part of the system using IT cyber security existing body of knowledge, or uses heuristics to inject attacks that could potentially harm the physical process. In this paper, we present a systematic approach to automatically generate integrity attacks from the CPS safety and control specifications, without knowledge of the physical system or its dynamics. The generated attacks violate the system operational and safety requirements, hence present a genuine test for system resilience. We present an algorithm to automate the malware payload development. Several examples are given throughout the paper to illustrate the proposed approach.
This study proposes a supervised learning method that does not rely on labels. We use variables associated with the label as indirect labels, and construct an indirect physics-constrained loss based on the physical mechanism to train the model. In the training process, the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix, and then the model is trained based on the indirect labels. The final prediction result of the model conforms to the physical mechanism between indirect label and label, and also meets the constraints of the indirect label. The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained. Finally, the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.
The lack of confirmation for the existence of supersymmetric particles and Weakly Interacting Massive Particles (WIMPs) appeals to extension of the field of studies of the physical nature of dark matter, involving non-supersymmetric and non-WIMP solutions. We briefly discuss some examples of such candidates in their relationship with extension of particle symmetry and pattern of symmetry breaking. We specify in the example of axion-like particles nontrivial features of cosmological reflection of the structure and pattern of Peccei-Quinn-like symmetry breaking. The puzzles of direct and indiect dark matter searches can find solution in the approach of composite dark matter. The advantages and open problems of this approach are specified. We note that detailed analysis of cosmological consequences of any extension of particle model that provides candidates for dark matter inevitably leads to nonstandard features in the corresponding cosmological scenario. It makes possible to use methods of cosmoparticle physics to study physical nature of the dark matter in the combination of its physical, astrophysical and cosmological signatures.
Experimental attempts to implement quantum speedup of computations over the past 30 years have yielded a negative result, despite the absence of physical laws prohibiting such speedup. The article formulates the limitation of quantum formalism in the form of uncertainty "the complexity of the system - the accuracy of its description at the quantum level", and provides arguments in favor of its physical status. An experiment to determine this constant through Grover's algorithm is described. Rough estimates on the constant of this ratio are given, based on the possibility of applying the quantum theory to two processes: the emission of a photon by a Rubidium atom and the decay of an unstable isotope of Helium 6. This ratio explicitly prohibits the physical implementation of scalable fast quantum computations, but leaves the possibility of modeling the dynamics of real systems on a quantum computer, the only advantage of which is the use of quantum nonlocality.
Dmitri Mogilevtsev, Yong-Siah Teo, Jaroslav Rehacek
et al.
The physical nature of any quantum source guarantees the existence of an effective Hilbert space of finite dimension, the physical sector, in which its state is completely characterized with arbitrarily high accuracy. The extraction of this sector is essential for state tomography. We show that the physical sector of a state, defined in some pre-chosen basis, can be systematically retrieved with a procedure using only data collected from a set of commuting quantum measurement outcomes, with no other assumptions about the source. We demonstrate the versatility and efficiency of the physical-sector extraction by applying it to simulated and experimental data for quantum light sources, as well as quantum systems of finite dimensions.
The possibility of fundamental theories with very many ground states, each with different physical parameters, changes the way that we approach the major questions of particle physics. Most importantly, it raises the possibility that these different parameters could be realised in different domains in the larger universe. In this review, we survey the motivations for the multiverse and impact of the idea of the multiverse on the search for new physics beyond the Standard Model.
Radiation transfer is an important topic in several physical disciplines, probably most prominently in astrophysics. Computer scientists use radiation transfer, among other things, for the visualisation of complex data sets with direct volume rendering. In this note, I point out the connection between physical radiation transfer and volume rendering, and I describe an implementation of direct volume rendering in the astrophysical radiation transfer code RADMC-3D. I show examples for the use of this module on analytical models and simulation data.
Despite the clear-cut prediction and subsequent experimental detection of the weak interaction bosons, the Higgs sector of the standard model of elementary particle physics has long remained one of its most obscure features. Here, it is demonstrated through a very basic argument that standard accounts of the Higgs mechanism suffer from a serious conceptual consistency problem, in that they incorrectly identify physical degrees of freedom. The point at issue, is that the reasoning which leads to a removal of the unphysical excitation modes is valid in both phases of the theory - i.e. both after and before the phase transition occurs. Consistently removing unphysical degrees of freedom implies a discrepancy in the number of physical degrees of freedom. In particular, the longitudinally polarized, massive gauge boson degrees of freedom do not have physical counterparts before the phase transition and are thus effectively "created ex nihilio" at the transition, within the context of ordinary Higgs models. Possible scenarios for removing the discrepancy are briefly considered. The results obtained here strongly indicate that although standard, perturbative formulations of the Higgs mechanism provide a convenient parametrization of electroweak physics over a certain range of scales, they cannot provide a sensible explanation of all relevant physical degrees of freedom involved.
The mathematical representation of the physical objects determines which mathematical branch will be applied during the physical analysis in the systems studied. The difference among non-quantum physics, like classic or relativistic physics, and quantum physics, especially in quantum field theory, is nothing else than the difference between the mathematics that is used on both branches of the physics. A common physical and mathematical origin for the analysis of the different systems brings both forms (quantum and classic) of understanding the nature mechanisms closer to each other.
We give an overview of the correspondance between one-time-physics and two-time-physics. This is characterized by the presence of an SO(d,2) symmetry and an Sp(2) duality among diverse one-time-physics systems all of which can be lifted to the same more symmetric two-time-physics system by the addition of gauge degrees of freedom. We provide several explicit examples of physical systems that support this correspondance. The example of a particle moving in (AdS_D) X (S^n), with SO(D+n,2) symmetry which is larger than the popularly known symmetry SO(D-1,2) X SO(n+1) for this case, should be of special current interest in view of the proposed AdS-CFT duality.
The purpose of this paper is to discuss the various types of physical universe which could exist according to modern mathematical physics. The paper begins with an introduction that approaches the question from the viewpoint of ontic structural realism. Section 2 takes the case of the 'multiverse' of spatially homogeneous universes, and analyses the famous Collins-Hawking argument, which purports to show that our own universe is a very special member of this collection. Section 3 considers the multiverse of all solutions to the Einstein field equations, and continues the discussion of whether the notions of special and typical can be defined within such a collection.