Time-dependent Input Research Articles

The importance of unsteady aerodynamics to road vehicle dynamics

This paper investigates the influence that different unsteady aerodynamic components have on a vehicle's handling. A simulated driver and vehicle are subject to two time-dependent crosswinds, one representative of a windy day and the second an extreme crosswind gust. Initially a quasi-static response is considered and then 5 additional sources of aerodynamic unsteadiness, based on experimental results, are added to the model.From the simulated vehicle and driver, the responses are used to produce results based on lateral deviation, driver steering inputs and also to create a ‘subjective’ handling rating. These results show that the largest effects are due to the relatively low frequency, time-dependent wind inputs. The additional sources of simulated unsteadiness have much smaller effect on the overall system and would be experienced as increased wind noise and reduced refinement rather than a worsening of the vehicle's handling.

Accuracy and response-time distributions for decision-making: linear perfect integrators versus nonlinear attractor-based neural circuits

Animals choose actions based on imperfect, ambiguous data. “Noise” inherent in neural processing adds further variability to this already-noisy input signal. Mathematical analysis has suggested that the optimal apparatus (in terms of the speed/accuracy trade-off) for reaching decisions about such noisy inputs is perfect accumulation of the inputs by a temporal integrator. Thus, most highly cited models of neural circuitry underlying decision-making have been instantiations of a perfect integrator. Here, in accordance with a growing mathematical and empirical literature, we describe circumstances in which perfect integration is rendered suboptimal. In particular we highlight the impact of three biological constraints: (1) significant noise arising within the decision-making circuitry itself; (2) bounding of integration by maximal neural firing rates; and (3) time limitations on making a decision. Under conditions (1) and (2), an attractor system with stable attractor states can easily best an integrator when accuracy is more important than speed. Moreover, under conditions in which such stable attractor networks do not best the perfect integrator, a system with unstable initial states can do so if readout of the system’s final state is imperfect. Ubiquitously, an attractor system with a nonselective time-dependent input current is both more accurate and more robust to imprecise tuning of parameters than an integrator with such input. Given that neural responses that switch stochastically between discrete states can “masquerade” as integration in single-neuron and trial-averaged data, our results suggest that such networks should be considered as plausible alternatives to the integrator model.

The effect of nonstationarity on models inferred from neural data

Neurons subject to a common nonstationary input may exhibit a correlated firing behavior. Correlations in the statistics of neural spike trains also arise as the effect of interaction between neurons. Here we show that these two situations can be distinguished with machine learning techniques, provided that the data are rich enough. In order to do this, we study the problem of inferring a kinetic Ising model, stationary or nonstationary, from the available data. We apply the inference procedure to two data sets: one from salamander retinal ganglion cells and the other from a realistic computational cortical network model. We show that many aspects of the concerted activity of the salamander retinal neurons can be traced simply to the external input. A model of non-interacting neurons subject to a nonstationary external field outperforms a model with stationary input with couplings between neurons, even accounting for the differences in the number of model parameters. When couplings are added to the nonstationary model, for the retinal data, little is gained: the inferred couplings are generally not significant. Likewise, the distribution of the sizes of sets of neurons that spike simultaneously and the frequency of spike patterns as a function of their rank (Zipf plots) are well explained by an independent-neuron model with time-dependent external input, and adding connections to such a model does not offer significant improvement. For the cortical model data, robust couplings, well correlated with the real connections, can be inferred using the nonstationary model. Adding connections to this model slightly improves the agreement with the data for the probability of synchronous spikes but hardly affects the Zipf plot.

Contaminant transport in soil with depth-dependent reaction coefficients and time-dependent boundary conditions

Predicting the fate and movement of contaminant in soils and groundwater is essential to assess and reduce the risk of soil contamination and groundwater pollution. Reaction processes of contaminant often decreased monotonously with depth. Time-dependent input sources usually occurred at the inlet of natural or human-made system such as radioactive waste disposal site. This study presented a one-dimensional convection–dispersion equation (CDE) for contaminant transport in soils with depth-dependent reaction coefficients and time-dependent inlet boundary conditions, and derived its analytical solution. The adsorption coefficient and degradation rate were represented as sigmoidal functions of soil depth. Solute breakthrough curves (BTCs) and concentration profiles obtained from CDE with depth-dependent and constant reaction coefficients were compared, and a constant effective reaction coefficient, which was calculated by arithmetically averaging the depth-dependent reaction coefficient, was proposed to reflect the lumped depth-dependent reaction effect. With the effective adsorption coefficient and degradation rate, CDE could produce similar BTCs and concentration profiles as those from CDE with depth-dependent reactions in soils with moderate chemical heterogeneity. In contrast, the predicted concentrations of CDE with fitted reaction coefficients at a certain depth departed significantly from those of CDE with depth-dependent reactions. Parametric analysis was performed to illustrate the effects of sinusoidally and exponentially decaying input functions on solute BTCs. The BTCs and concentration profiles obtained from the solutions for finite and semi-infinite domain were compared to investigate the effects of effluent boundary condition. The finite solution produced higher concentrations at the increasing limb of the BTCs and possessed a higher peak concentration than the semi-infinite solution which had a slightly long tail. Furthermore, the finite solution gave a higher concentration in the immediate vicinity of the exit boundary than the semi-infinite solution. The applicability of the proposed model was tested with a field herbicide and tracer leaching experiment in an agricultural area of northeastern Greece. The simulation results indicated that the proposed CDE with depth-dependent reaction coefficients was able to capture the evolution of metolachlor concentration at the upper soil depths. However, the simulation results at deep depths were not satisfactory as the proposed model did not account for preferential flow observed in the field.

Feedback by massive stars and the emergence of superbubbles

We study the evolution of isolated and merging interstellar bubbles of three stars (25, 32 and 60 M\odot) in a homogeneous background medium with a density of 10 mp /ccm via 3D-hydrodynamic simulations with standard ISM thermodynamics (optically thin radiative cooling and photo-electric heating) and time dependent energy and mass input according to stellar evolutionary tracks. We vary the position of the three stars relative to each other to compare the energy response for cases of isolated, merging and initially cospatial bubbles. Due to mainly the Vishniac instability, our simulated bubbles develop thick shells and filamentary internal structures in column density. The shell widths reach tens of per cent of the outer bubble radius, which compares favourably to observations. More energy is retained in the ISM for more closely packed groups, by up to a factor of three and typically a factor of two for intermediate times after the first supernova. Once the superbubble is established, different positions of the contained stars make only a minor difference to the energy tracks. For our case of three massive stars, the energy deposition varies only very little for distances up to about 30 pc between the stars. Energy injected by supernovae is entirely dissipated in a superbubble on a timescale of about 1 Myr, which increases slightly with the superbubble size at the time of the explosion. The Vishniac instability may be responsible for the broadening of the shells of interstellar bubbles. Massive star winds are significant energetically due to their - in the long run - more efficient, steady energy injection and because they evacuate the space around the massive stars. For larger scale simulations, the feedback effect of close groups of stars or clusters may be subsumed into one effective energy input with insignificant loss of energy accuracy.

A population pharmacokinetic model for the complex systemic absorption of ropivacaine after femoral nerve block in patients undergoing knee surgery

Because of its slow systemic absorption and flip-flop kinetics, ropivacaine's pharmacokinetics after a peripheral nerve block has never been thoroughly characterized. The purpose of this study was to develop a population pharmacokinetic model for ropivacaine after loco-regional administration and to identify patient characteristics that may influence the drug's absorption and disposition. Frequent plasma samples were taken up to 93h after a 100mg dose given as femoral block for postoperative analgesia in 15 orthopedic patients. Ropivacaine plasma concentration-time data were analyzed using a nonlinear mixed effects modeling method. A one-compartment model with parallel inverse Gaussian and time-dependent inputs best described ropivacaine plasma concentration-time curves. Ropivacaine systemic absorption was characterized by a rapid phase (mean absorption time of 25±4.8min) followed by a much slower phase (half-life of 3.9±0.65h). Interindividual variability (IIV) for these parameters, 58 and 9%, indicated that the initial absorption phase was more variable. The apparent volume of distribution (V/F=77.2±11.5L, IIV=26%) was influenced by body weight (Δ 1.49% per kg change) whereas the absorption rate constant (slower phase) of ropivacaine was affected by age (Δ 2.25% per year change). No covariate effects were identified for the apparent clearance of the drug (CL/F =10.8±1.0L/h, 34 IIV=34 %). These findings support our hypothesis that modeling a complex systemic absorption directly from plasma concentration-time curves exhibiting flip-flop kinetics is possible. Only the age-effect was considered as relevant for possible dosing adjustments.

Comprehensive estimation of input signals and dynamics in biochemical reaction networks

Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function.Parameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation. For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedure is suboptimal since the input uncertainties are not considered in the estimation process which often leads to overoptimistic confidence intervals of the inferred parameters and the model dynamics.Results: This article presents a new approach which includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously. We applied this comprehensive approach to an illustrative model with simulated data and compared it to alternative methods. Statistical analyses revealed that our method improves the prediction of the model dynamics and the confidence intervals leading to a proper coverage of the confidence intervals of the dynamic parameters. The method was applied to the JAK-STAT signaling pathway.Availability: MATLAB code is available on the authors' website http://www.fdmold.uni-freiburg.de/~schelker/.Contact: max.schelker@fdm.uni-freiburg.deSupplementary Information: Additional information is available at Bioinformatics Online.

Transient conjugate heat transfer in straight microchannels

Many theoretical and experimental studies have been carried out in order to study the flow and heat transfer in microchannels. In the recent years, numerical simulation has been applied to investigate the problem under a variety of conditions. However, much of the focus has been on steady-state problems and time-dependent transport has received relatively minor attention, despite its importance in practical electronic devices and systems. Employing a versatile commercial code, this paper aims to examine heat removal from straight rectangular microchannels affected by a time-dependent heat flux input. Both cosinusoidal variation and step-change application and removal of a uniform heat source are studied to determine the response time of the system. For the fluid phase, the two-dimensional momentum and energy equations are solved, considering temperature dependent properties and viscous dissipation. The effects of the amplitude of the heat flux variation, inlet velocity, and geometry, including the thickness of the heat sink, are investigated. Channels of smaller width are found to be more sensitive to the heat flux source, especially for higher input values. The velocity represents the most important parameter for channels of greatest width considered here, as it directly affects the fluid dynamics and the pressure drop when a time-dependent heat source is applied to the system.

The Input Signal Step Function (ISSF), a Standard Method to Encode Input Signals in SBML Models with Software Support, Applied to Circadian Clock Models

Time-dependent light input is an important feature of computational models of the circadian clock. However, publicly available models encoded in standard representations such as the Systems Biology Markup Language (SBML) either do not encode this input or use different mechanisms to do so, which hinders reproducibility of published results as well as model reuse. The authors describe here a numerically continuous function suitable for use in SBML for models of circadian rhythms forced by periodic light-dark cycles. The Input Signal Step Function (ISSF) is broadly applicable to encoding experimental manipulations, such as drug treatments, temperature changes, or inducible transgene expression, which may be transient, periodic, or mixed. It is highly configurable and is able to reproduce a wide range of waveforms. The authors have implemented this function in SBML and demonstrated its ability to modify the behavior of publicly available models to accurately reproduce published results. The implementation of ISSF allows standard simulation software to reproduce specialized circadian protocols, such as the phase-response curve. To facilitate the reuse of this function in public models, the authors have developed software to configure its behavior without any specialist knowledge of SBML. A community-standard approach to represent the inputs that entrain circadian clock models could particularly facilitate research in chronobiology.

The generalized amalgam method for modal reduction

In this paper, we propose an extension of the amalgam method to reduce advection diffusion problem with time dependent parameters. From a full general basis, this technique consists to select rapidly the most influent modes, and to add to them the remaining modes balanced by a coefficient. The originality of this paper lies in the fast obtention of a full degraded model, used as a reference in a minimization process of the reduced model’s error. Transcribed to the physical space, this error approaches the error between the reduced model and the real physical finite elements model. The treated application is a disk rotating at a variable velocity with time dependent thermal inputs. Comparison between the reduced and the finite elements model gives a gain in term of computational time of 36 for a mean error on the entire domain equal to 0.42%.

Dynamics of Networks of Excitatory and Inhibitory Neurons in Response to Time-Dependent Inputs

We investigate the dynamics of recurrent networks of excitatory (E) and inhibitory (I) neurons in the presence of time-dependent inputs. The dynamics is characterized by the network dynamical transfer function, i.e., how the population firing rate is modulated by sinusoidal inputs at arbitrary frequencies. Two types of networks are studied and compared: (i) a Wilson–Cowan type firing rate model; and (ii) a fully connected network of leaky integrate-and-fire (LIF) neurons, in a strong noise regime. We first characterize the region of stability of the “asynchronous state” (a state in which population activity is constant in time when external inputs are constant) in the space of parameters characterizing the connectivity of the network. We then systematically characterize the qualitative behaviors of the dynamical transfer function, as a function of the connectivity. We find that the transfer function can be either low-pass, or with a single or double resonance, depending on the connection strengths and synaptic time constants. Resonances appear when the system is close to Hopf bifurcations, that can be induced by two separate mechanisms: the I–I connectivity and the E–I connectivity. Double resonances can appear when excitatory delays are larger than inhibitory delays, due to the fact that two distinct instabilities exist with a finite gap between the corresponding frequencies. In networks of LIF neurons, changes in external inputs and external noise are shown to be able to change qualitatively the network transfer function. Firing rate models are shown to exhibit the same diversity of transfer functions as the LIF network, provided delays are present. They can also exhibit input-dependent changes of the transfer function, provided a suitable static non-linearity is incorporated.

MPC of constrained discrete-time linear periodic systems — A framework for asynchronous control: Strong feasibility, stability and optimality via periodic invariance

State-feedback model predictive control (MPC) of discrete-time linear periodic systems with time-dependent state and input dimensions is considered. The states and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. First, periodic controlled and positively invariant sets are characterized, and a method to determine the maximum periodic controlled and positively invariant sets is derived. The proposed periodic controlled invariant sets are then employed in the design of least-restrictive strongly feasible reference-tracking MPC problems. The proposed periodic positively invariant sets are employed in combination with well-known results on optimal unconstrained periodic linear-quadratic regulation (LQR) to yield constrained periodic LQR control laws that are stabilizing and optimal. One motivation for systems with time-dependent dimensions is efficient control law synthesis for discrete-time systems with asynchronous inputs, for which a novel modeling framework resulting in low dimensional models is proposed. The presented methods are applied to a multirate nano-positioning system.

A renewable, modular, and time-responsive DNA circuit

In this article, we introduce a new design for DNA logic gates based on enzymatic restriction of DNA strands. We present a construction for a set of one and two-input logic gates and argue that our construction can be generalized to implement any Boolean operation. A key feature of our design is its time-responsiveness, in the presence of appropriate fuels our circuit can operate continuously and generate a time-dependent output in response to a time-dependent input. Moreover, modulo connectivity information, the strand design and circuit design phases are decoupled.

Estimating discontinuous periodic signals in a time inhomogeneous diffusion

We consider a diffusion \({(\xi_t)_{t\geq 0}}\) with some T-periodic time dependent input term contained in the drift: under an unknown parameter \({\vartheta\in\varTheta}\) , some discontinuity—an additional periodic signal—occurs at times \({kT\,{+}\,\vartheta}\) , \({k \in I\!\!N}\) . Assuming positive Harris recurrence of \({(\xi_{kT})_{k \in I\!\!N _0}}\) and exploiting the periodicity structure, we prove limit theorems for certain martingales and functionals of the process \({(\xi_t)_{t\ge 0}}\) . They allow to consider the statistical model parametrized by \({\vartheta\in\varTheta}\) locally in small neighbourhoods of some fixed \({\vartheta}\), with radius \({\frac{1}{n}}\) as n → ∞. We prove convergence of local models to a limit experiment studied by Ibragimov and Khasminskii (Statistical estimation, 1981) and discuss the behaviour of estimators under contiguous alternatives.

Time-dependent coupled mode analysis of parallel waveguides

Coupled mode theory for parallel waveguides is extended to include temporal variations of both the dielectric function of the photonic array and the input optical power. This formulation can be very useful for the design and comprehensive analysis of modern photonic devices, such as two-dimensional photonic crystals, represented by arrays of parallel waveguides. In the special case of a time-dependent input signal, but stationary dielectric constant, analytical solutions exist for the extended formulation. The accuracy and computer time of the formulation's numerical solutions are examined against finite difference time domain and time-dependent beam propagation analyses of waveguide arrays.

Non-equilibrium encoding of time-dependent input signals by neurons with effective refractoriness

Event Abstract Back to Event Non-equilibrium encoding of time-dependent input signals by neurons with effective refractoriness Moritz Deger1*, Moritz Helias2, Stefano Cardanobile1, Fatihcan M. Atay3 and Stefan Rotter1 1 Albert-Ludwig University, Bernstein Center Freiburg & Faculty of Biology, Germany 2 Brain Science Institute, RIKEN, Japan 3 Max Planck Institute for Mathematics in the Sciences, MPI, Germany Spike trains emitted by cortical neurons can be effectively described by stochastic point processes. The Poisson process with absolute refractoriness is a particularly simple point process, which admits the mathematical analysis of its dynamic properties. As we show, through the generalization to randomly distributed refractory times (Fig. A), this process can represent a large class of well-known renewal processes, including certain types of gamma- and log-normal processes. These are frequently used models for stationary neuronal spike trains. To study the non-equilibrium dynamics of ensembles of such processes we employ a description in terms of the occupation numbers of two states: Active and refractory. These occupation numbers follow a distributed delay differential equation. Based on this dynamics, the time-dependent spike rate of an ensemble of processes encoding an arbitrary input trajectory can be studied. In particular, we consider the case of a step change of the input rate, which induces a stochastic transient of the output spike rate (Fig. B). Moreover, for the periodic response to periodic input profiles, we obtain the mapping of the discrete spectra of input and output, by which all harmonics are coupled linearly, with coefficients depending on the refractoriness. For the special case of cosine input and a fixed refractory time, we find resonances, phase jumps and frequency doubling in the time-dependent ensemble spike rate. Since a large class of renewal processes can be represented as Poisson processes with random refractory time, our approach represents a widely applicable framework to define and analyze non-stationary point process models of neural activity. Figure 1: Ensemble of Poisson processes with random refractoriness, with different widths of the refractory time distribution. A: Probability density of refractory time (dotted lines) and normalized hazard function for constant input rate (solid lines). B: Transients of the ensemble rate upon step change of the input rate at t=0. Theoretical result (solid lines) and simulation of an ensemble of 106 processes averaged over 225 trials (crosses). Error bars denote the standard deviation over trials. Figure 1 Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Presentation Topic: Bernstein Conference on Computational Neuroscience Citation: Deger M, Helias M, Cardanobile S, Atay FM and Rotter S (2010). Non-equilibrium encoding of time-dependent input signals by neurons with effective refractoriness. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00024 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 20 Sep 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Moritz Deger, Albert-Ludwig University, Bernstein Center Freiburg & Faculty of Biology, Freiburg, Germany, md-web@gmx.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Moritz Deger Moritz Helias Stefano Cardanobile Fatihcan M Atay Stefan Rotter Google Moritz Deger Moritz Helias Stefano Cardanobile Fatihcan M Atay Stefan Rotter Google Scholar Moritz Deger Moritz Helias Stefano Cardanobile Fatihcan M Atay Stefan Rotter PubMed Moritz Deger Moritz Helias Stefano Cardanobile Fatihcan M Atay Stefan Rotter Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Dynamics of a bistable Mott insulator to superfluid phase transition in cavity optomechanics

We study the dynamics of the many-body state of ultracold bosons trapped in a bistable optical lattice in an optomechanical resonator controlled by a time-dependent input field. We focus on the dynamics of the many-body system following discontinuous jumps of the intracavity field. We identify experimentally realizable parameters for the bistable quantum phase transition between Mott insulator and superfluid.

Quasistatic approximation of the interspike interval distribution of neurons driven by time-dependent inputs

Variability in neural responses is a ubiquitous phenomenon in neurons, usually modeled with stochastic differential equations. In particular, stochastic integrate-and-fire models are widely used to simplify theoretical studies. The statistical properties of the generated spikes depend on the stimulating input current. Given that real sensory neurons are driven by time-dependent signals, here we study how the interspike interval distribution of integrate-and-fire neurons depends on the evolution of the stimulus in a quasistatic limit. We obtain a closed-form expression for this distribution, and we compare it to the one obtained with numerical simulations for several time-dependent currents. For slow inputs, the quasistatic distribution provides a very good description of the data. The results obtained for the integrate-and-fire model can be extended to other nonautonomous stochastic systems where the first passage time problem has an explicit solution.

Bursting neurons encode the time-dependent phase of the input signals

Cortical cells operate in different firing modes. Fast spiking cells and regular spiking neurons transform the input synaptic current into fairly regular spike trains. In sensory areas, these spike sequences are known to encode information about external stimuli. Instead, intrinsically bursting neurons exhibit a stereotyped firing behaviour. During slow-wave sleep and epileptic seizures, bursting activity propagates throughout the cortex producing large-amplitude oscillations. Since these rhythmic patterns also arise in culture tissue, synchronous bursting in healthy subjects was originally thought to play only a regulatory role, mainly associated with unconscious processes.However, in the last decade cortical bursting was also reported to encode sensory information. For example, in cat striate cortex, the length of each burst was shown to be strongly modulated by stimulus orientation [1]. There are other examples in monkey visual cortex, rat barrel cortex and rodent hippocampus [2-4]. In these cases, the activity pattern of the bursting population was different from the one observed during slow-wave sleep: cells were not synchronized, and individual responses were irregular, with a variable number of spikes per burst. In this framework, here we aim at understanding the way in which such burst-mediated codes represent sensory information. To that end, we explore a computational model of a bursting cortical cell driven by different types of input currents. We show that there is a tight correspondence between the number of spikes per burst and the phase of the driving current at burst onset. This is also applicable to stochastic time-dependent input currents, where a downstream decoder reading out the number of spikes per burst may predict the time-dependent phase of the input signal with a precision of roughly p/8.Our results may help to interpret phase-mediated codes in cortex. For example, in the rodent hippocampus, bursting pyramidal cells encode the location of the animal within the place field in the phase difference between the theta-oscillating local field potential (LFP) and burst initiation [4]. Similarly, cells in primate visual cortex encode significant amounts of sensory information in the phase of firing relative to the LFP [5]. We show that these phase-mediated codes are a consequence of the resonator and integrator behaviour of bursting pyramidal neurons. We also present a robust mechanism to read out the input phase at the time of burst firing that simply consists of counting the number of spikes per discharge.

Influence of clamping on distortion of welded S355 T-joints

Welding almost always leads to some distortion of a structure due to its strongly time dependent and localised heat input. This distortion is usually undesirable and causes problems related to alignment and fit-up tolerances with adjacent structures. Considerable research has been carried out on different mitigation methods to reduce distortion to manageable levels; however, most of this has been for a single, simple set of clamping conditions. In practice, clamping is often required and the choice of restraint conditions and subsequent influence on distortion should be explored. This paper examines gas metal arc welds made on S355 T-joints under different clamping conditions. Measured welding temperatures and subsequent part distortions are shown for two different clamping conditions and compared to simulations. The interaction of the clamps and the workpiece due to the material reaction is examined by recording the transient clamping force. It is shown that the clamping condition strongly affects the welding distortion amplitude and might even alter the distortion mode.